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Dongping Ming
Polytechnic Center for Natural Resources Big-Data, Ministry of Natural Resources of China, Beijing 100036, China

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Journal article
Published: 04 June 2021 in Remote Sensing
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Aircraft is a means of transportation and weaponry, which is crucial for civil and military fields to detect from remote sensing images. However, detecting aircraft effectively is still a problem due to the diversity of the pose, size, and position of the aircraft and the variety of objects in the image. At present, the target detection methods based on convolutional neural networks (CNNs) lack the sufficient extraction of remote sensing image information and the post-processing of detection results, which results in a high missed detection rate and false alarm rate when facing complex and dense targets. Aiming at the above questions, we proposed a target detection model based on Faster R-CNN, which combines multi-angle features driven and majority voting strategy. Specifically, we designed a multi-angle transformation module to transform the input image to realize the multi-angle feature extraction of the targets in the image. In addition, we added a majority voting mechanism at the end of the model to deal with the results of the multi-angle feature extraction. The average precision (AP) of this method reaches 94.82% and 95.25% on the public and private datasets, respectively, which are 6.81% and 8.98% higher than that of the Faster R-CNN. The experimental results show that the method can detect aircraft effectively, obtaining better performance than mature target detection networks.

ACS Style

Fengcheng Ji; Dongping Ming; Beichen Zeng; Jiawei Yu; Yuanzhao Qing; Tongyao Du; Xinyi Zhang. Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN. Remote Sensing 2021, 13, 2207 .

AMA Style

Fengcheng Ji, Dongping Ming, Beichen Zeng, Jiawei Yu, Yuanzhao Qing, Tongyao Du, Xinyi Zhang. Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN. Remote Sensing. 2021; 13 (11):2207.

Chicago/Turabian Style

Fengcheng Ji; Dongping Ming; Beichen Zeng; Jiawei Yu; Yuanzhao Qing; Tongyao Du; Xinyi Zhang. 2021. "Aircraft Detection in High Spatial Resolution Remote Sensing Images Combining Multi-Angle Features Driven and Majority Voting CNN." Remote Sensing 13, no. 11: 2207.

Journal article
Published: 17 March 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Landslide susceptibility mapping (LSM) is an effective way to predict spatial probability of landslide occurrence. Existing convolutional neural networks (CNN)-based methods apply self-built CNN with simple structure, which failed to reach CNN's full potential on high-level feature extraction, meanwhile ignored the use of numerical predisposing factors. For the purpose of exploring feature fusion based CNN models with greater reliability in LSM, this study proposes an ensemble model based on channel-expanded pre-trained CNN and traditional machine learning model (CPCNN-ML). In CPCNN-ML, pre-trained CNN with mature structure is modified to excavate high-level features of multi-channel predisposing factor layers. LSM result is generated by traditional machine learning (ML) model based on hybrid feature of high-level features and numerical predisposing factors. Lantau Island, Hong Kong is selected as study area, temporal landslide inventory is used for model training and evaluation. Experimental results show that CPCNN-ML has ability to predict landslide occurrence with high reliability, especially the CPCNN-ML based on random forest (RF). Contrast experiments with self-built CNN and traditional ML models further embody the superiority of CPCNN-ML. It is worth noting that coastal regions are newly identified landslide-prone regions compared with previous research. This finding is of great reference value for Hong Kong authorities to formulate appropriate disaster prevention and mitigation policies.

ACS Style

Yangyang Chen; Dongping Ming; Xiao Ling; Xianwei Lv; Chenghu Zhou. Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 3625 -3639.

AMA Style

Yangyang Chen, Dongping Ming, Xiao Ling, Xianwei Lv, Chenghu Zhou. Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):3625-3639.

Chicago/Turabian Style

Yangyang Chen; Dongping Ming; Xiao Ling; Xianwei Lv; Chenghu Zhou. 2021. "Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 3625-3639.

Journal article
Published: 09 February 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Identifying the spatial structure of lunar impact craters is necessary to increase our understanding of past geologic processes on the Moon. However, detecting multiscale spatial structures of craters in images in appropriate resolutions using optimum scale parameters has not been quantified. This paper presents a semivariogram approach for this purpose. The range of the semivariogram model represents the minimum average size of the crater type detected in an image of a spatial resolution. The feature lag distances of the semivariogram model indicate that a series of appropriate spatial resolutions rather than a single spatial resolution are required to address multiscale lunar impact crater structures. The optimum scale parameters for delineating multiscale crater structures in segmentation are constrained by the range and feature lag distances derived from semivariogram of the corresponding image in a certain spatial resolution. This research fills the gap in quantifying multiscale spatial structure of impact craters using semivariogram analysis for optimizing object-based crater mapping.

ACS Style

Jiao Wang; Dongping Ming; Weiming Cheng. Identification of Multiscale Spatial Structure of Lunar Impact Crater: A Semivariogram Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Jiao Wang, Dongping Ming, Weiming Cheng. Identification of Multiscale Spatial Structure of Lunar Impact Crater: A Semivariogram Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Jiao Wang; Dongping Ming; Weiming Cheng. 2021. "Identification of Multiscale Spatial Structure of Lunar Impact Crater: A Semivariogram Approach." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 28 March 2020 in Remote Sensing
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The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.

ACS Style

Hanqing Bao; Dongping Ming; Ya Guo; Kui Zhang; Keqi Zhou; Shigao Du. DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data. Remote Sensing 2020, 12, 1088 .

AMA Style

Hanqing Bao, Dongping Ming, Ya Guo, Kui Zhang, Keqi Zhou, Shigao Du. DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data. Remote Sensing. 2020; 12 (7):1088.

Chicago/Turabian Style

Hanqing Bao; Dongping Ming; Ya Guo; Kui Zhang; Keqi Zhou; Shigao Du. 2020. "DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data." Remote Sensing 12, no. 7: 1088.

Journal article
Published: 19 November 2019 in Remote Sensing of Environment
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Functional zone reflects city's spatial structures, and as a carrier of social and economic activities, it is of critical significance to urban management, resource allocation and planning. However, most researches on functional zone division are based on a large spatial scale such as blocks or other scales larger than it. Aiming at a subtle fine functional result, the concept of Super Object (SO) was especially explained, also a Super Object - Convolutional Neural Network (SO–CNN) based urban functional zone fine division method with very high resolution (VHR) remote sensing image was proposed. The original image was firstly segmented into different SOs which correspond to the basic functional zone units in geography. A random point generation algorithm was used to generate the voting points for functional zone category identification, and then a trained CNN model was employed to assign functional attributes to those voting points. Then a statistical method was involved to count the frequency of the classified voting points of different functional attributes in each basic functional zone units. By voting process, the functional attribute with the highest frequency was assigned to the basic functional zone unit, which corrected the misclassification results of CNN to some extent. This paper also explored the scale effect of the SO on the final functional zone classification result from two aspects, spatial scale of SO and the sampling window size of CNN model. Because of the natural differences between functional zone division and land cover classification, region based overall accuracy assessment method was used to evaluate functional zone division result. Compared with other methods, SO–CNN method can generate higher accuracy and subtle result, based on which larger spatial scale results can be available by scaling-up, so SO–CNN method plays a great significant role on small scale functional space structure research.

ACS Style

Wen Zhou; Dongping Ming; Xianwei Lv; Keqi Zhou; Hanqing Bao; Zhaoli Hong. SO–CNN based urban functional zone fine division with VHR remote sensing image. Remote Sensing of Environment 2019, 236, 111458 .

AMA Style

Wen Zhou, Dongping Ming, Xianwei Lv, Keqi Zhou, Hanqing Bao, Zhaoli Hong. SO–CNN based urban functional zone fine division with VHR remote sensing image. Remote Sensing of Environment. 2019; 236 ():111458.

Chicago/Turabian Style

Wen Zhou; Dongping Ming; Xianwei Lv; Keqi Zhou; Hanqing Bao; Zhaoli Hong. 2019. "SO–CNN based urban functional zone fine division with VHR remote sensing image." Remote Sensing of Environment 236, no. : 111458.

Journal article
Published: 24 October 2019 in Remote Sensing
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Change detection (CD) remains an important issue in remote sensing applications, especially for high spatial resolution (HSR) images, but it has yet to be fully resolved. This work proposes a novel object-based change detection (OBCD) method for HSR images that is based on region–line primitive association analysis and evidence fusion. In the proposed method, bitemporal images are separately segmented, and the segmentation results are overlapped to obtain the temporal region primitives (TRPs). The temporal line primitives (TLPs) are obtained by straight line detection on bitemporal images. In the initial CD stage, Dempster–Shafer evidence theory fuses the multiple items of evidence of the TRPs’ spectrum, edge, and gradient changes, and obtains the initial changed areas. In the refining CD stage, the association between the TRPs and their contacting TLPs in the unchanged areas is established on the basis of the region–line primitive association framework, and the TRPs’ main line directions (MLDs) are calculated. Some changed TRPs omitted in the initial CD stage are recovered by their MLD changes, thereby refining the initial CD results. Different from common OBCD methods, the proposed method considers the change evidence of TRPs’ internal and boundary information simultaneously via information complementation between TRPs and TLPs. The proposed method can significantly reduce missed alarms while maintaining a low level of false alarms in OBCD, thereby improving total accuracy. In our experiments, our method is superior to common CD methods, including change vector analysis (CVA), PCA-k-means, and iterative reweighted multivariate alteration detection (IRMAD), in terms of overall accuracy, missed alarms, and Kappa coefficient.

ACS Style

Jiru Huang; Yang Liu; Min Wang; Yalan Zheng; Dongping Ming. Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion. Remote Sensing 2019, 11, 2484 .

AMA Style

Jiru Huang, Yang Liu, Min Wang, Yalan Zheng, Dongping Ming. Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion. Remote Sensing. 2019; 11 (21):2484.

Chicago/Turabian Style

Jiru Huang; Yang Liu; Min Wang; Yalan Zheng; Dongping Ming. 2019. "Change Detection of High Spatial Resolution Images Based on Region-Line Primitive Association Analysis and Evidence Fusion." Remote Sensing 11, no. 21: 2484.

Research article
Published: 16 September 2019 in Journal of Spectroscopy
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Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.

ACS Style

Min Cao; Dongping Ming; Lu Xu; Ju Fang; Lin Liu; Xiao Ling; Weizhi Ma. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis. Journal of Spectroscopy 2019, 2019, 1 -15.

AMA Style

Min Cao, Dongping Ming, Lu Xu, Ju Fang, Lin Liu, Xiao Ling, Weizhi Ma. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis. Journal of Spectroscopy. 2019; 2019 ():1-15.

Chicago/Turabian Style

Min Cao; Dongping Ming; Lu Xu; Ju Fang; Lin Liu; Xiao Ling; Weizhi Ma. 2019. "Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis." Journal of Spectroscopy 2019, no. : 1-15.

Journal article
Published: 02 September 2019 in Remote Sensing
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Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and hinders further improvements in classification results, due to the insufficiency of information provided by very high-spatial resolution images (VHSRIs). To be specific, the phenomenon of different objects with similar spectrum and the lack of topographic information (heights) are natural drawbacks of VHSRIs. Thus, multisource data steps into people’s sight and shows a promising future. Firstly, for data fusion, this paper proposed a standard normalized digital surface model (StdnDSM) method which was actually a digital elevation model derived from a digital terrain model (DTM) and digital surface model (DSM) to break through the bottleneck by fusing VHSRI and cloud points. It smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification. The fusion data then were utilized to perform multiresolution segmentation (MRS) and worked as training data for the CNN. Moreover, the grey-level co-occurrence matrix (GLCM) was introduced for a stratified MRS. Secondly, for data processing, the stratified MRS was more efficient than unstratified MRS, and its outcome result was theoretically more rational and explainable than traditional global segmentation. Eventually, classes of segmented polygons were determined by majority voting. Compared to pixel-based and traditional object-based classification methods, majority voting strategy has stronger robustness and avoids misclassifications caused by minor misclassified centre points. Experimental analysis results suggested that the proposed method was promising for object-based classification.

ACS Style

Keqi Zhou; Dongping Ming; Xianwei Lv; Ju Fang; Min Wang. CNN-based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data. Remote Sensing 2019, 11, 2065 .

AMA Style

Keqi Zhou, Dongping Ming, Xianwei Lv, Ju Fang, Min Wang. CNN-based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data. Remote Sensing. 2019; 11 (17):2065.

Chicago/Turabian Style

Keqi Zhou; Dongping Ming; Xianwei Lv; Ju Fang; Min Wang. 2019. "CNN-based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data." Remote Sensing 11, no. 17: 2065.

Research article
Published: 10 April 2019 in Earth Science Informatics
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Traditional classification methods, which use low-level features, have failed to gain satisfactory classification results of very high spatial resolution (VHR) remote sensing images. Even though per-pixel classification method based on convolutional neural network (CNN) (Per-pixel CNN) achieved higher accuracy with the help of high-level features, this method still has limitations. Per-superpixel classification method based on CNN (Per-superpixel CNN) overcomes the limitations of per-pixel CNN, however, there are still some scale related issues in per-superpixel CNN needed to be explored and addressed. Firstly, in order to avoid the misclassification of complex land cover objects caused by scale effect, the per-superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) is proposed. Secondly, this paper analyzes how scale parameter of CNN impacts the classification accuracy and involves spatial statistics to pre-estimate scale parameter in per-superpixel CNN. This paper takes two VHR remote sensing images as experimental data, and employs two superpixel segmentation algorithms to classify urban and suburban land covers. The experimental results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent. Additionally, through discussion of the influence of accuracy evaluation method in CNN classification, it is stressed that random selection of ground truth validation points from study area is recommended and more responsibly other than using part of a reference dataset.

ACS Style

Yangyang Chen; Dongping Ming; Xianwei Lv. Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation. Earth Science Informatics 2019, 12, 341 -363.

AMA Style

Yangyang Chen, Dongping Ming, Xianwei Lv. Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation. Earth Science Informatics. 2019; 12 (3):341-363.

Chicago/Turabian Style

Yangyang Chen; Dongping Ming; Xianwei Lv. 2019. "Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation." Earth Science Informatics 12, no. 3: 341-363.

Journal article
Published: 09 January 2019 in Remote Sensing
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Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

ACS Style

Lu Xu; Dongping Ming; Wen Zhou; Hanqing Bao; Yangyang Chen; Xiao Ling. Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation. Remote Sensing 2019, 11, 108 .

AMA Style

Lu Xu, Dongping Ming, Wen Zhou, Hanqing Bao, Yangyang Chen, Xiao Ling. Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation. Remote Sensing. 2019; 11 (2):108.

Chicago/Turabian Style

Lu Xu; Dongping Ming; Wen Zhou; Hanqing Bao; Yangyang Chen; Xiao Ling. 2019. "Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation." Remote Sensing 11, no. 2: 108.

Articles
Published: 11 December 2018 in International Journal of Remote Sensing
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Object-based image analysis (OBIA) is the mainstream technique for the analysis of high-spatial-resolution (HSR) images. However, routine OBIAs often follow a region-based technical framework, which limits their performance in remote sensing information extraction. In this study, a more flexible OBIA technical framework and methods are designed to extract a man-made object, i.e., docks, from HSR images. The proposed method includes the following steps. 1) Waters are extracted by object-based land/water classification, and buffer zones around the shorelines are built to limit the dock searching. 2) Edge line primitives (ELPs) for dock extraction are obtained from the shorelines by edge scanning and are closed into region primitives (RPs) for region-based OBIA. 3) Straight line primitives (SLPs) in contact with the RPs are extracted and spatial relationships between the RPs and SLPs are built based on the region-line primitive association framework (RLPAF). 4) Docks are then detected and refined by RLPAF features. Different with routine OBIAs, RPs are not simply obtained by image segmentation. The proposed line-to-region conversion prevents the influence of segmentation errors and imprecise segment boundaries and makes RPs accurate in morphology. In addition, synergetic analyses involving multiple region and line primitives make a flexible OBIA and improve its performance. The proposed method is tested using China’s Gaofen-2 multispectral images with spatial resolution of 4 m, and compared with the results obtained with eCognition’s rule-based classification. Experiments show that the proposed method can extract docks from HSR images with much better accuracy than routine OBIAs. In the three experimental areas, accuracy measures such as precision, recall, F-measure and boundary recall are more than 0.90, 0.95, 0.95, and 0.85, respectively.

ACS Style

Jie Wang; Jiru Huang; Min Wang; Dongping Ming. Dock extraction from China’s Gaofen-2 multispectral imagery based on region-line primitive association analyses. International Journal of Remote Sensing 2018, 40, 3878 -3899.

AMA Style

Jie Wang, Jiru Huang, Min Wang, Dongping Ming. Dock extraction from China’s Gaofen-2 multispectral imagery based on region-line primitive association analyses. International Journal of Remote Sensing. 2018; 40 (10):3878-3899.

Chicago/Turabian Style

Jie Wang; Jiru Huang; Min Wang; Dongping Ming. 2018. "Dock extraction from China’s Gaofen-2 multispectral imagery based on region-line primitive association analyses." International Journal of Remote Sensing 40, no. 10: 3878-3899.

Journal article
Published: 04 December 2018 in Remote Sensing
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Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.

ACS Style

Xianwei Lv; Dongping Ming; Tingting Lu; Keqi Zhou; Min Wang; Hanqing Bao. A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification. Remote Sensing 2018, 10, 1946 .

AMA Style

Xianwei Lv, Dongping Ming, Tingting Lu, Keqi Zhou, Min Wang, Hanqing Bao. A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification. Remote Sensing. 2018; 10 (12):1946.

Chicago/Turabian Style

Xianwei Lv; Dongping Ming; Tingting Lu; Keqi Zhou; Min Wang; Hanqing Bao. 2018. "A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification." Remote Sensing 10, no. 12: 1946.

Journal article
Published: 01 November 2018 in Photogrammetric Engineering & Remote Sensing
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The quality of multi-scale segmentation mainly consists of intrasegment homogeneity and intersegment heterogeneity; however, it is difficult to synchronously get both high. It is crucial to make it clear which one of these two measures is more important and what is the coupling relationship among segmentation scale parameter, image segmentation and classification accuracy. This paper employs series of segmentation and classification to show that (1) intrasegment homogeneity is more important than intersegment heterogeneity in GeOBIA; there is always highly positive correlation between intrasegment homogeneity and classification accuracy; (2) with the increase of spectral heterogeneity parameter, both image object amount and the intrasegment homogeneity decrease; however the intersegment heterogeneity increases or increases first then decrease after the appropriate scale; and (3) the appropriate scale means there is a compromise between intrasegment homogeneity and intersegment heterogeneity. The research findings are helpful to raise awareness among practitioners who suffer from scale issues in GeOBIA.

ACS Style

Dongping Ming; Wen Zhou; Lu Xu; Min Wang; Yanni Ma. Coupling Relationship Among Scale Parameter, Segmentation Accuracy, and Classification Accuracy In GeOBIA. Photogrammetric Engineering & Remote Sensing 2018, 84, 681 -693.

AMA Style

Dongping Ming, Wen Zhou, Lu Xu, Min Wang, Yanni Ma. Coupling Relationship Among Scale Parameter, Segmentation Accuracy, and Classification Accuracy In GeOBIA. Photogrammetric Engineering & Remote Sensing. 2018; 84 (11):681-693.

Chicago/Turabian Style

Dongping Ming; Wen Zhou; Lu Xu; Min Wang; Yanni Ma. 2018. "Coupling Relationship Among Scale Parameter, Segmentation Accuracy, and Classification Accuracy In GeOBIA." Photogrammetric Engineering & Remote Sensing 84, no. 11: 681-693.

Review
Published: 01 October 2018 in Photogrammetric Engineering & Remote Sensing
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Image segmentation is a key technique involved in information extraction from high spatial resolution remote sensing images. Studying the impact of the evaluation method on the segmentation result is equally as important as studying the segmentation algorithm itself. However, research in segmentation evaluation is behind that of segmentation algorithms. Only a few review articles about segmentation evaluation were published in computer vision field. Therefore, reviewing segmentation evaluation methods used for high spatial resolution remote sensing images is of great significance. This paper summarizes widely used evaluation methods in remote sensing field, analyzes their advantages and shortcomings, and discusses their application range. Especially this paper uses series of experiments to demonstrate the supervised and unsupervised image segmentation evaluation process and analyzes the performance of some commonly used supervised and unsupervised evaluation indexes. Further, potential applications and possible future direction for high spatial resolution remote sensing image segmentation evaluation are finally summarized.

ACS Style

Yangyang Chen; Dongping Ming; Lu Zhao; Beiru Lv; Keqi Zhou; Yuanzhao Qing. Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation. Photogrammetric Engineering & Remote Sensing 2018, 84, 629 -646.

AMA Style

Yangyang Chen, Dongping Ming, Lu Zhao, Beiru Lv, Keqi Zhou, Yuanzhao Qing. Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation. Photogrammetric Engineering & Remote Sensing. 2018; 84 (10):629-646.

Chicago/Turabian Style

Yangyang Chen; Dongping Ming; Lu Zhao; Beiru Lv; Keqi Zhou; Yuanzhao Qing. 2018. "Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation." Photogrammetric Engineering & Remote Sensing 84, no. 10: 629-646.

Articles
Published: 20 September 2018 in International Journal of Remote Sensing
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Pixel-based convolutional neural network (CNN) has demonstrated good performance in the classification of very high resolution images (VHRI) from which abstract deep features are extracted. However, conventional pixel-based CNN demands large resources in terms of processing time and disk space. Therefore, superpixel CNN classification has recently become a focus of attention. We therefore propose a CNN based deep learning method combining superpixels extracted via energy-driven sampling (SEEDS) for VHRI classification. The approach consists of three main steps. First, based on the concept of geographic object-based image analysis (GEOBIA), the image is segmented into homogeneous superpixels using the SEEDS based superpixel segmentation method thereby decreasing the number of processing units. Second, the training data and testing data are extracted from the image and concatenated on a superpixel level at a variety of scales for CNN. Third, the training data are input to train the parameters of CNN and abstract deep features are extracted from the VHRI. Using these extracted deep features, we classify two VHRI data sets at single scales and multiple scales. To verify the effectiveness of SEEDS based CNN classification, the performance of SEEDS and three others superpixel segmentation algorithms are compared, and the superpixel extraction via SEEDS method was found to be the optimal superpixel segmentation approach for CNN classification. The scale effect on CNN classification accuracy was investigated by comparing the four superpixel segmentation methods. We found that (1) There is no strong evidence that using scales combinations is better than a single scale in some specific situations; (2) Natural objects with low complexity are not as sensitive to scale as artificial objects; (3) For a simple VHRI that contains clear artificial objects and simple texture, the classification result with multiple scales performs better a the single scale; (4) In contrast, for the complex VHRI containing a large number of complex objects, the classification result with a single small-scale best.

ACS Style

Xianwei Lv; Dongping Ming; Yangyang Chen; Min Wang. Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification. International Journal of Remote Sensing 2018, 40, 506 -531.

AMA Style

Xianwei Lv, Dongping Ming, Yangyang Chen, Min Wang. Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification. International Journal of Remote Sensing. 2018; 40 (2):506-531.

Chicago/Turabian Style

Xianwei Lv; Dongping Ming; Yangyang Chen; Min Wang. 2018. "Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification." International Journal of Remote Sensing 40, no. 2: 506-531.

Journal article
Published: 19 September 2018 in Remote Sensing
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As the basic feature of building, building edges play an important role in many fields such as urbanization monitoring, city planning, surveying and mapping. Building edges detection from high spatial resolution remote sensing (HSRRS) imagery has always been a long-standing problem. Inspired by the recent success of deep-learning-based edge detection, a building edge detection model using a richer convolutional features (RCF) network is employed in this paper to detect building edges. Firstly, a dataset for building edges detection is constructed by the proposed most peripheral constraint conversion algorithm. Then, based on this dataset the RCF network is retrained. Finally, the edge probability map is obtained by RCF-building model, and this paper involves a geomorphological concept to refine edge probability map according to geometric morphological analysis of topographic surface. The experimental results suggest that RCF-building model can detect building edges accurately and completely, and that this model has an edge detection F-measure that is at least 5% higher than that of other three typical building extraction methods. In addition, the ablation experiment result proves that using the most peripheral constraint conversion algorithm can generate more superior dataset, and the involved refinement algorithm shows a higher F-measure and better visual effect contrasted with the non-maximal suppression algorithm.

ACS Style

Tingting Lu; Dongping Ming; Xiangguo Lin; Zhaoli Hong; Xueding Bai; Ju Fang. Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network. Remote Sensing 2018, 10, 1496 .

AMA Style

Tingting Lu, Dongping Ming, Xiangguo Lin, Zhaoli Hong, Xueding Bai, Ju Fang. Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network. Remote Sensing. 2018; 10 (9):1496.

Chicago/Turabian Style

Tingting Lu; Dongping Ming; Xiangguo Lin; Zhaoli Hong; Xueding Bai; Ju Fang. 2018. "Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network." Remote Sensing 10, no. 9: 1496.

Journal article
Published: 27 August 2018 in IEEE Access
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The extraction and vectorization of roads from high spatial resolution remote sensing (HSRRS) images are of great significance to city planning and development. However, significant as they are, it is usually an arduous task to put them into practice because the HSRRS images are often filled with complex ground information. Furthermore, extracted roads may suffer from netsplit or brokenness. This paper thus proposes Richer convolutional features (RCFs)-based road extraction (Road-RCF) as a method which targets these issues. A modified roads sample set and RCF network are applied to generate road probabilities in order to extract initial road information. After the road centerlines extraction by the refinement algorithm, vectorized roads are ultimately extracted. The compared experiment results show that the Road-RCF method produce better road extraction results than the other four state-of-the-art methods, in both quantitative road extraction accuracy metrics and the qualitative visual evaluation. The benefits of this model are threefold. First, the image-to-image network structure of side-output realizes multi-scale and multi-level road feature fusion in order to make a full use of the information from a low level to a high level. Second, according to the deep supervision of the side-output, it guides the learning of the correct road information. Third, after the detection of the road, the road centerlines are vectorized to facilitate the attribute information management and electronic map production. In a word, the proposed Road-RCF method is both practical and meaningful toward updating the geo-information system database.

ACS Style

Zhaoli Hong; Dongping Ming; Keqi Zhou; Ya Guo; Tingting Lu. Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features. IEEE Access 2018, 6, 46988 -47000.

AMA Style

Zhaoli Hong, Dongping Ming, Keqi Zhou, Ya Guo, Tingting Lu. Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features. IEEE Access. 2018; 6 ():46988-47000.

Chicago/Turabian Style

Zhaoli Hong; Dongping Ming; Keqi Zhou; Ya Guo; Tingting Lu. 2018. "Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features." IEEE Access 6, no. : 46988-47000.

Research article
Published: 03 June 2018 in Journal of Spectroscopy
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The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.

ACS Style

Wen Zhou; Dongping Ming; Lu Xu; Hanqing Bao; Min Wang. Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division. Journal of Spectroscopy 2018, 2018, 1 -11.

AMA Style

Wen Zhou, Dongping Ming, Lu Xu, Hanqing Bao, Min Wang. Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division. Journal of Spectroscopy. 2018; 2018 ():1-11.

Chicago/Turabian Style

Wen Zhou; Dongping Ming; Lu Xu; Hanqing Bao; Min Wang. 2018. "Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division." Journal of Spectroscopy 2018, no. : 1-11.

Journal article
Published: 01 June 2017 in Computers & Geosciences
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Scale problems are a major source of concern in the field of remote sensing. Since the remote sensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remote sensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remote sensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remote sensing data and it is helpful to analyze the observation scale from different aspects. This research will ultimately benefit for remote sensing data selection and application. The general understanding of pixel-based scale effect in remote sensing.Spatial resolution is used in FD to analyze the pixel-based scale effect.Using information entropy to evaluate the modified FD results.Feature scales on FD curves are related to the actual sizes of the geo-object.Ultimately benefit for remote sensing data selection and application.

ACS Style

Guixiang Feng; Dongping Ming; Min Wang; Jianyu Yang. Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method. Computers & Geosciences 2017, 103, 183 -190.

AMA Style

Guixiang Feng, Dongping Ming, Min Wang, Jianyu Yang. Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method. Computers & Geosciences. 2017; 103 ():183-190.

Chicago/Turabian Style

Guixiang Feng; Dongping Ming; Min Wang; Jianyu Yang. 2017. "Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method." Computers & Geosciences 103, no. : 183-190.

Journal article
Published: 01 January 2017 in ISPRS Journal of Photogrammetry and Remote Sensing
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ACS Style

Min Wang; Qi Cui; Jie Wang; Dongping Ming; Guonian Lv. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features. ISPRS Journal of Photogrammetry and Remote Sensing 2017, 123, 104 -113.

AMA Style

Min Wang, Qi Cui, Jie Wang, Dongping Ming, Guonian Lv. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features. ISPRS Journal of Photogrammetry and Remote Sensing. 2017; 123 ():104-113.

Chicago/Turabian Style

Min Wang; Qi Cui; Jie Wang; Dongping Ming; Guonian Lv. 2017. "Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features." ISPRS Journal of Photogrammetry and Remote Sensing 123, no. : 104-113.