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Jiantao Liu
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

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Journal article
Published: 28 April 2019 in Remote Sensing
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Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

ACS Style

Quanlong Feng; Jianyu Yang; Dehai Zhu; Jiantao Liu; Hao Guo; Batsaikhan Bayartungalag; Baoguo Li. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sensing 2019, 11, 1006 .

AMA Style

Quanlong Feng, Jianyu Yang, Dehai Zhu, Jiantao Liu, Hao Guo, Batsaikhan Bayartungalag, Baoguo Li. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta. Remote Sensing. 2019; 11 (9):1006.

Chicago/Turabian Style

Quanlong Feng; Jianyu Yang; Dehai Zhu; Jiantao Liu; Hao Guo; Batsaikhan Bayartungalag; Baoguo Li. 2019. "Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta." Remote Sensing 11, no. 9: 1006.

Journal article
Published: 06 November 2015 in Sustainability
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Natural deltas can provide human beings with flat and fertile land to be cultivated. It is important to monitor cropland dynamics to provide policy-relevant information for regional sustainable development. This paper utilized Landsat imagery to study the cropland dynamics of the Yellow River Delta during the last three decades. Multi-temporal Landsat data were used to account for the phenological variations of different plants. Several spectral and textural features were adopted to increase the between-class separability. The robust random forest classifier was used to generate the land cover maps of the Yellow River Delta for 1986, 1995, 2005 and 2015. Experimental results indicated that the proposed methodology showed good performance with an average classification accuracy of 89.44%. The spatial-temporal analysis indicated that the cropland area increased from 467.6 km2 in 1986 to 718.5 km2 in 2015 with an average growth rate of 8.65 km2/year. The newly created croplands were mainly due to the reclamation of grassland and bare soil while the losses of croplands were due to abandoned cultivation and urban sprawl. The results demonstrate that a sustainable perspective should be adopted by the decision makers in order to simultaneously maintain food security, industrial development and ecosystem safety.

ACS Style

Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability 2015, 7, 14834 -14858.

AMA Style

Quanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015. Sustainability. 2015; 7 (11):14834-14858.

Chicago/Turabian Style

Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Monitoring Cropland Dynamics of the Yellow River Delta based on Multi-Temporal Landsat Imagery over 1986 to 2015." Sustainability 7, no. 11: 14834-14858.

Journal article
Published: 23 September 2015 in Remote Sensing
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Remote sensing is recognized as a valuable tool for flood mapping due to its synoptic view and continuous coverage of the flooding event. This paper proposed a hybrid approach based on multiple endmember spectral analysis (MESMA) and Random Forest classifier to extract inundated areas in Yuyao City in China using medium resolution optical imagery. MESMA was adopted to tackle the mixing pixel problem induced by medium resolution data. Specifically, 35 optimal endmembers were selected to construct a total of 3111 models in the MESMA procedure to derive accurate fraction information. A multi-dimensional feature space was constructed including the normalized difference water index (NDWI), topographical parameters of height, slope, and aspect together with the fraction maps. A Random Forest classifier consisting of 200 decision trees was adopted to classify the post-flood image based on the above multi-features. Experimental results indicated that the proposed method can extract the inundated areas precisely with a classification accuracy of 94% and a Kappa index of 0.88. The inclusion of fraction information can help improve the mapping accuracy with an increase of 2.5%. Moreover, the proposed method also outperformed the maximum likelihood classifier and the NDWI thresholding method. This research provided a useful reference for flood mapping using medium resolution optical remote sensing imagery.

ACS Style

Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing 2015, 7, 12539 -12562.

AMA Style

Quanlong Feng, Jianhua Gong, Jiantao Liu, Yi Li. Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China. Remote Sensing. 2015; 7 (9):12539-12562.

Chicago/Turabian Style

Quanlong Feng; Jianhua Gong; Jiantao Liu; Yi Li. 2015. "Flood Mapping Based on Multiple Endmember Spectral Mixture Analysis and Random Forest Classifier—The Case of Yuyao, China." Remote Sensing 7, no. 9: 12539-12562.

Original articles
Published: 21 August 2015 in Remote Sensing Letters
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Accurate estimation of phytoplankton chlorophyll-a (chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-a concentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-a concentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [Rrs−1(661.6) – Rrs−1(706.7)] [Rrs−1(714.8) – Rrs−1(682.2)]−1 was calibrated after band tuning, where Rrs−1 represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-a concentration with a root mean square error (RMSE) of 1.47 μg l−1. To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-a variation with an RMSE of 1.35 μg l−1. Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-a concentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery.

ACS Style

Quanlong Feng; Jianhua Gong; Ying Wang; Jiantao Liu; Yi Li; A.N. Ibrahim; Qigen Liu; Zhongjun Hu. Estimating chlorophyll- a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China. Remote Sensing Letters 2015, 6, 735 -744.

AMA Style

Quanlong Feng, Jianhua Gong, Ying Wang, Jiantao Liu, Yi Li, A.N. Ibrahim, Qigen Liu, Zhongjun Hu. Estimating chlorophyll- a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China. Remote Sensing Letters. 2015; 6 (10):735-744.

Chicago/Turabian Style

Quanlong Feng; Jianhua Gong; Ying Wang; Jiantao Liu; Yi Li; A.N. Ibrahim; Qigen Liu; Zhongjun Hu. 2015. "Estimating chlorophyll- a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China." Remote Sensing Letters 6, no. 10: 735-744.

Journal article
Published: 31 March 2015 in Water
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Flooding is a severe natural hazard, which poses a great threat to human life and property, especially in densely-populated urban areas. As one of the fastest developing fields in remote sensing applications, an unmanned aerial vehicle (UAV) can provide high-resolution data with a great potential for fast and accurate detection of inundated areas under complex urban landscapes. In this research, optical imagery was acquired by a mini-UAV to monitor the serious urban waterlogging in Yuyao, China. Texture features derived from gray-level co-occurrence matrix were included to increase the separability of different ground objects. A Random Forest classifier, consisting of 200 decision trees, was used to extract flooded areas in the spectral-textural feature space. Confusion matrix was used to assess the accuracy of the proposed method. Results indicated the following: (1) Random Forest showed good performance in urban flood mapping with an overall accuracy of 87.3% and a Kappa coefficient of 0.746; (2) the inclusion of texture features improved classification accuracy significantly; (3) Random Forest outperformed maximum likelihood and artificial neural network, and showed a similar performance to support vector machine. The results demonstrate that UAV can provide an ideal platform for urban flood monitoring and the proposed method shows great capability for the accurate extraction of inundated areas.

ACS Style

Quanlong Feng; Jiantao Liu; Jianhua Gong. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water 2015, 7, 1437 -1455.

AMA Style

Quanlong Feng, Jiantao Liu, Jianhua Gong. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China. Water. 2015; 7 (12):1437-1455.

Chicago/Turabian Style

Quanlong Feng; Jiantao Liu; Jianhua Gong. 2015. "Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China." Water 7, no. 12: 1437-1455.

Journal article
Published: 19 January 2015 in Remote Sensing
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Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.

ACS Style

Quanlong Feng; Jiantao Liu; Jianhua Gong. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing 2015, 7, 1074 -1094.

AMA Style

Quanlong Feng, Jiantao Liu, Jianhua Gong. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing. 2015; 7 (1):1074-1094.

Chicago/Turabian Style

Quanlong Feng; Jiantao Liu; Jianhua Gong. 2015. "UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis." Remote Sensing 7, no. 1: 1074-1094.