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Convolutional neural networks (CNNs) have shown great success in a variety of real-world applications and the outstanding performance of the state-of-the-art CNNs is primarily driven by the elaborate architecture. Evolutionary convolutional neural network (ECNN) is a promising approach to design the optimal CNN architecture automatically. Nevertheless, most of the existing ECNN methods only focus on improving the performance of the discovered CNN architectures without considering the relevance between different classification tasks. Transfer learning is a human-like learning approach and has been introduced to solve complex problems in the domain of evolutionary algorithms (EAs). In this paper, an effective ECNN optimization method with cross-tasks transfer strategy (CTS) is proposed to facilitate the evolution process. The proposed method is then evaluated on benchmark image classification datasets as a case study. The experimental results show that the proposed method can not only speed up the evolutionary process significantly but also achieve competitive classification accuracy. To be specific, our proposed method can reach the same accuracy at least 40 iterations early and an improvement of accuracy for 0.88% and 3.12% on MNIST-FASHION and CIFAR10 datasets compared with ECNN, respectively.
Zhao Wang; Di Lu; Huabing Wang; Tongfei Liu; Peng Li. Evolutionary Convolutional Neural Network Optimization with Cross-Tasks Transfer Strategy. Electronics 2021, 10, 1857 .
AMA StyleZhao Wang, Di Lu, Huabing Wang, Tongfei Liu, Peng Li. Evolutionary Convolutional Neural Network Optimization with Cross-Tasks Transfer Strategy. Electronics. 2021; 10 (15):1857.
Chicago/Turabian StyleZhao Wang; Di Lu; Huabing Wang; Tongfei Liu; Peng Li. 2021. "Evolutionary Convolutional Neural Network Optimization with Cross-Tasks Transfer Strategy." Electronics 10, no. 15: 1857.
Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available at https://github.com/TongfeiLiu/ASEA-CD.
Zhiyong Lv; Fengjun Wang; Tongfei Liu; Xiangbin Kong; Jon Atli Benediktsson. Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleZhiyong Lv, Fengjun Wang, Tongfei Liu, Xiangbin Kong, Jon Atli Benediktsson. Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleZhiyong Lv; Fengjun Wang; Tongfei Liu; Xiangbin Kong; Jon Atli Benediktsson. 2021. "Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Lv ZhiYong; Tongfei Liu; Jon Atli Benediktsson; Nicola Falco. Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review. IEEE Geoscience and Remote Sensing Magazine 2021, PP, 2 -21.
AMA StyleLv ZhiYong, Tongfei Liu, Jon Atli Benediktsson, Nicola Falco. Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review. IEEE Geoscience and Remote Sensing Magazine. 2021; PP (99):2-21.
Chicago/Turabian StyleLv ZhiYong; Tongfei Liu; Jon Atli Benediktsson; Nicola Falco. 2021. "Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review." IEEE Geoscience and Remote Sensing Magazine PP, no. 99: 2-21.
Landslide inventory mapping (LIM) on the basis of change detection techniques has potential significance for landslide disaster analysis. In this letter, a novel LIM approach based on the adaptive histogram-mean distance (AHMD) is proposed, which adaptively considers spatial contextual information of different landslide regions to improve the detection performance. First, to adapt the shape, size, and distribution of various landslides, an adaptive region around a pixel is extracted by a novel adaptive region extension algorithm without parameter setting. Second, the pixels within the adaptive region are taken to construct the spectral frequency histograms, and then, the adaptive histogram mean (AHM) is developed as the feature of a histogram. Third, the AHMD is defined based on the bin-to-bin (B2B) distance to measure change magnitude between the pairwise AHMs. Finally, LIM can be obtained by a supervised threshold method called double-window flexible pace search (DFPS). Experimental results tested on two real datasets with a very high spatial resolution (VHR) demonstrate the outperformance of the proposed AHMD approach with seven comparative methods.
Tongfei Liu; Maoguo Gong; Fenlong Jiang; Yuanqiao Zhang; Hao Li. Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance With Bitemporal VHR Aerial Images. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleTongfei Liu, Maoguo Gong, Fenlong Jiang, Yuanqiao Zhang, Hao Li. Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance With Bitemporal VHR Aerial Images. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleTongfei Liu; Maoguo Gong; Fenlong Jiang; Yuanqiao Zhang; Hao Li. 2021. "Landslide Inventory Mapping Method Based on Adaptive Histogram-Mean Distance With Bitemporal VHR Aerial Images." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Landslide inventory mapping (LIM) plays an important role in landslide susceptibility analysis. Many LIM approaches based on change detection techniques have been proposed, but with various drawbacks. For example, existing approaches have limited capability to capture the objects of varying shapes/sizes present in an area impacted by landslide. Many existing approaches are supervised and require parameter tuning. Moreover, some methods are prone to salt-and-pepper noise. To overcome these limitations, in this letter, an algorithm based on automatic adaptive region extension using very-high-resolution remote sensing images is developed. First, a simple yet effective k-means clustering method is used to generate training samples for landslide and nonlandslide classes, which refer to changed and unchanged areas, respectively. Second, an automatic adaptive region extension algorithm is developed and applied to each pixel of the postevent image, and the label of an extended region around a pixel is determined by the nearest distance between the central pixel and the changed or unchanged samples. Finally, the labels of a pixel are recorded because a pixel in different adaptive regions may be reassigned dissimilar labels, and the final label of the pixel is consistent with its maximum assigned label. To verify the performance of the proposed approach, we conducted experiments on two different landslide sites with VHR remote sensing images in Lantau Island, Hong Kong, China. Experimental results clearly demonstrate that the proposed approach has several advantages in improving the performance of LIM with VHR remote sensing images.
Lv Zhiyong; Tongfei Liu; Robert Yu Wang; Jon Atli Benediktsson; Sudipan Saha. Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleLv Zhiyong, Tongfei Liu, Robert Yu Wang, Jon Atli Benediktsson, Sudipan Saha. Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleLv Zhiyong; Tongfei Liu; Robert Yu Wang; Jon Atli Benediktsson; Sudipan Saha. 2020. "Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
The majority of the change detection (CD) methods consider spatial information by using a regular window or strict mathematical model. Moreover, these methods use the spectra directly to measure the change magnitude between bitemporal images. To solve this problem, local histogram-based analysis (LHBA) is proposed for detecting a land cover change in this letter. This new approach aims to inhibit the pseudo change by defining the local histogram trend (LHT) in an adaptive manner instead of using spectral values to measure change magnitude directly. In the proposed approach, the spatial information around each pixel is first exploited by defining an adaptive local histogram. The LHT distance between the pairwise local histograms is then developed to measure the change magnitude between the pairwise pixels of bitemporal images. Finally, the change magnitude image is generated, and a binary CD is achieved by a threshold method. Experiments based on two pairs of very high-resolution remote sensing images, which refer to land use change and landslides events, demonstrate the advantages and performance of the proposed approach.
Zhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson. Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1284 -1287.
AMA StyleZhiyong Lv, Tongfei Liu, Cheng Shi, Jon Atli Benediktsson. Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (7):1284-1287.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson. 2020. "Local Histogram-Based Analysis for Detecting Land Cover Change Using VHR Remote Sensing Images." IEEE Geoscience and Remote Sensing Letters 18, no. 7: 1284-1287.
This study presents a novel dual-path full convolutional network (DP-FCN) model for constructing a landslide inventory map (LIM) with bitemporal high-resolution remote sensing images. Unlike traditional methods for drawing LIM, the proposed DP-FCN directly draws LIMs from the bitemporal aerial images through a trained deep neural network without generating the change magnitude map. Thus, the proposed approach can effectively reduce the effects of pseudo changes caused by phenological differences rather than landslide events. The proposed DP-FCN model contains two modules, namely, deep feature extraction and joint feature learning networks. Deep feature extraction aims to reduce redundancy while extracting the high-level deep features from bitemporal images. Joint feature learning establishes the relationship between the deep features of bitemporal images and the ground reference map. Experiments on the real datasets of the landslide sites in Lantau Island of Hong Kong, China demonstrate the feasibility and superiority of the proposed approach in drawing LIM with very high-resolution remote sensing images. Moreover, compared with the results obtained by the state-of-the-art algorithms, the proposed DP-FCN method achieves the best performance in terms of accuracy for landslide inventory mapping.
Zhiyong Lv; Tongfei Liu; Xiangbing Kong; Cheng Shi; Jon Atli Benediktsson. Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4575 -4584.
AMA StyleZhiyong Lv, Tongfei Liu, Xiangbing Kong, Cheng Shi, Jon Atli Benediktsson. Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4575-4584.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Xiangbing Kong; Cheng Shi; Jon Atli Benediktsson. 2020. "Landslide Inventory Mapping With Bitemporal Aerial Remote Sensing Images Based on the Dual-Path Fully Convolutional Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4575-4584.
Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-the-art methods in terms of visual performance and quantitative measurements.
Zhiyong Lv; Tongfei Liu; Jon Atli Benediktsson. Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 6524 -6533.
AMA StyleZhiyong Lv, Tongfei Liu, Jon Atli Benediktsson. Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (9):6524-6533.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Jon Atli Benediktsson. 2020. "Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 9: 6524-6533.
Detecting land cover change through very-high-resolution (VHR) remote sensing images is helpful in supporting urban sustainable development, natural disaster evaluation, and environmental assessment. However, the intraclass spectral variance in VHR remote sensing images is usually larger than that of median-low remote sensing images. Furthermore, the bitemporal images are usually acquired under different atmospheric conditions, sun height, soil moisture, and other factors. Consequently, in practical applications, many pseudo changes are presented in the detected map. In this paper, an adaptive histogram trend (AHT) similarity approach is promoted to quantitatively measure the magnitude between the corresponding pixels in bitemporal images in terms of change semantic. In the proposed approach, to reduce the phenological effect on the bitemporal images of land cover change detection (LCCD), we first define the quantitative description of AHT. Second, the change magnitudes between pairwise pixels are quantitatively measured by an improved bin-to-bin (B2B) distance between the corresponding AHTs. Then, the change magnitudes between two entire bitemporal images are measured AHT-by-AHT. Finally, binary threshold methods, such as the Otsu method or the double-window flexible pace search (DFPS) method, are used to divide the change magnitude image into binary change detection maps and obtain the final change detection map. The performance of the AHT-based LCCD approach is verified by four pairs of VHR remote-sensing images that correspond to two types of real land cover change cases. The detected results based on the four pairs of bitemporal VHR images outperformed the compared state-of-the-art LCCD methods.
Zhi Yong Lv; Tong Fei Liu; Penglin Zhang; Jon Atli Benediktsson; Tao Lei; Xiaokang Zhang. Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 9554 -9574.
AMA StyleZhi Yong Lv, Tong Fei Liu, Penglin Zhang, Jon Atli Benediktsson, Tao Lei, Xiaokang Zhang. Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (12):9554-9574.
Chicago/Turabian StyleZhi Yong Lv; Tong Fei Liu; Penglin Zhang; Jon Atli Benediktsson; Tao Lei; Xiaokang Zhang. 2019. "Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 57, no. 12: 9554-9574.
Land cover change detection (LCCD) based on bitemporal remote sensing images has become a popular topic in the field of remote sensing. Despite numerous methods promoted in recent decades, an improvement on the usability and performance of these methods is still required. In this study, a novel LCCD approach based on the integration of K-means clustering and adaptive majority voting (Kmeans_ AMV) techniques has been developed. The proposed K-means_AMV method consists of three major techniques. First, to utilize the contextual information in an adaptive manner, an adaptive region around a central pixel is constructed by detecting the spectral similarity between a central pixel and its eight neighboring pixels. Second, when the extension for an adaptive region is terminated, the K-means clustering method is applied to determine the label of each pixel within the adaptive region. Finally, an existing AMV technique is used to refine the label of the central pixel of the adaptive region. When change magnitude image (CMI) is scanned and processed in this manner, the label of each pixel in the CMI can be refined, and the binary change detection map can be generated. Three image scenes related to different land cover change events are adopted to test the effectiveness and performance of the proposed K-means_AMV approach. Compared with several widely used methods, experimental results clearly demonstrated that the propose K-means_AMV approach not only can achieve better detection accuracy but also has a better performance in vision.
Zhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson; Hejuan Du. Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images. IEEE Access 2019, 7, 34425 -34437.
AMA StyleZhiyong Lv, Tongfei Liu, Cheng Shi, Jon Atli Benediktsson, Hejuan Du. Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images. IEEE Access. 2019; 7 (99):34425-34437.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Cheng Shi; Jon Atli Benediktsson; Hejuan Du. 2019. "Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images." IEEE Access 7, no. 99: 34425-34437.
To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
Zhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing 2018, 10, 1809 .
AMA StyleZhiyong Lv, Tongfei Liu, Jón Atli Benediktsson, Tao Lei, Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10 (11):1809.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. 2018. "Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images." Remote Sensing 10, no. 11: 1809.
Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.
Zhiyong Lv; Tongfei Liu; Penglin Zhang; Jón Atli Benediktsson; Yixiang Chen. Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images. Remote Sensing 2018, 10, 901 .
AMA StyleZhiyong Lv, Tongfei Liu, Penglin Zhang, Jón Atli Benediktsson, Yixiang Chen. Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images. Remote Sensing. 2018; 10 (6):901.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Penglin Zhang; Jón Atli Benediktsson; Yixiang Chen. 2018. "Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images." Remote Sensing 10, no. 6: 901.
In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.
Zhiyong Lv; Tongfei Liu; Yiliang Wan; Jón Atli Benediktsson; Xiaokang Zhang. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sensing 2018, 10, 472 .
AMA StyleZhiyong Lv, Tongfei Liu, Yiliang Wan, Jón Atli Benediktsson, Xiaokang Zhang. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10 (3):472.
Chicago/Turabian StyleZhiyong Lv; Tongfei Liu; Yiliang Wan; Jón Atli Benediktsson; Xiaokang Zhang. 2018. "Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images." Remote Sensing 10, no. 3: 472.