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Fu Chen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

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Journal article
Published: 16 August 2020 in Geomorphology
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In ancient China, the course of rivers changed frequently under the influence of the natural environment. The study of changes in paleochannels provides the basis for river regulation and water conservancy, and also supplies an important scientific foundation for the evolution of the geographical environment. However, it is extremely difficult to determine the location of paleochannels due to the limitations of traditional research methods and modern remote sensing interpretation. This paper introduces a new method, Detrended Digital Elevation Model Interpretation, for supporting interpretation of ancient rivers at large scales. A detrended DEM was obtained by removing the overall trend from the original DEM by subtracting a “detrending surface” derived from the original image using a guiding filter. The paleochannels of the Yellow River in the North China Plain were interpreted by GIS analysis with the help of historical materials. The detrended DEM was found to be less affected by the surface environment and had higher identification accuracy compared with an optical image and the colored DEM. Moreover, the detrended DEM could be used to interpret ancient rivers at large scales. Finally, the flow path of ancient rivers in the Eastern Han Dynasty (or near the Jindi River) was hypothesized using the interpretation results. The resulting paleochannel data can be used to revise historical river maps and provide reference for researching and managing the Yellow River.

ACS Style

Shuyan Zhang; Yong Ma; Fu Chen; Jianbo Liu; Fulong Chen; Shanlong Lu; Liyuan Jiang; Delong Li. A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation. Geomorphology 2020, 369, 107374 .

AMA Style

Shuyan Zhang, Yong Ma, Fu Chen, Jianbo Liu, Fulong Chen, Shanlong Lu, Liyuan Jiang, Delong Li. A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation. Geomorphology. 2020; 369 ():107374.

Chicago/Turabian Style

Shuyan Zhang; Yong Ma; Fu Chen; Jianbo Liu; Fulong Chen; Shanlong Lu; Liyuan Jiang; Delong Li. 2020. "A new method for supporting interpretation of paleochannels in a large scale — Detrended Digital Elevation Model Interpretation." Geomorphology 369, no. : 107374.

Journal article
Published: 24 March 2020 in Remote Sensing
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Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them.

ACS Style

Liyuan Jiang; Yong Ma; Fu Chen; Jianbo Liu; Wutao Yao; Yubao Qiu; Shuyan Zhang. Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors. Remote Sensing 2020, 12, 1043 .

AMA Style

Liyuan Jiang, Yong Ma, Fu Chen, Jianbo Liu, Wutao Yao, Yubao Qiu, Shuyan Zhang. Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors. Remote Sensing. 2020; 12 (6):1043.

Chicago/Turabian Style

Liyuan Jiang; Yong Ma; Fu Chen; Jianbo Liu; Wutao Yao; Yubao Qiu; Shuyan Zhang. 2020. "Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors." Remote Sensing 12, no. 6: 1043.

Journal article
Published: 02 January 2020 in Remote Sensing
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Ice storms greatly affect the structure, dynamics, and functioning of forest ecosystems. Studies on the impact of such disasters, as well as the post-disaster recovery of forests, are important contents in forest biology, ecology, and geography. Remote-sensing technology provides data and methods that can support the study of disasters at the large-to-medium scale and over long time periods. This study took Chebaling National Nature Reserve in Guangdong Province, China, as the study area. First, field-survey data and remote-sensing data were comprehensively analyzed to demonstrate the feasibility of replacing the forest stock volume with the mean annual value of the Enhanced Vegetation Index (EVI), to study forest growth and change. We then used the EVI from 2007 to 2017, together with a variety of other remote-sensing and forest sub-compartment data, to analyze the impact of the 2008 ice storm and the subsequent post-disaster recovery of the forest. Finally, we drew the following conclusions: (1) Topography had a considerable effect on disaster impact and forest recovery in Chebaling. The forest at high altitudes (700–1000 m) and on steep slopes (25–40°) was seriously affected by this disaster but had a stronger post-disaster recovery ability. Meanwhile, the hardest-hit area for coniferous forest was higher and steeper than that for broad-leaved forest. (2) In the same terrain conditions, coniferous forests were less affected by the disaster than broad-leaved forests and showed less variation during the post-disaster recovery process. Nevertheless, broad-leaved forests had faster recovery rates and higher recovery degrees; (3) Under the influence of human activities, the recovery and fluctuation degree for planted forest in the post-disaster recovery process was significantly higher than that for natural forest. The study suggests that forest has high disaster resistance and self-recovery ability after the ice storm, and this ability has a strong correlation with the type of forest and the topographic factors such as elevation and slope. At the same time, human intervention can speed up the recovery of forests after disasters.

ACS Style

Wutao Yao; Yong Ma; Fu Chen; Zhishu Xiao; Zufei Shu; Lijun Chen; Wenhong Xiao; Jianbo Liu; Liyuan Jiang; Shuyan Zhang. Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China. Remote Sensing 2020, 12, 164 .

AMA Style

Wutao Yao, Yong Ma, Fu Chen, Zhishu Xiao, Zufei Shu, Lijun Chen, Wenhong Xiao, Jianbo Liu, Liyuan Jiang, Shuyan Zhang. Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China. Remote Sensing. 2020; 12 (1):164.

Chicago/Turabian Style

Wutao Yao; Yong Ma; Fu Chen; Zhishu Xiao; Zufei Shu; Lijun Chen; Wenhong Xiao; Jianbo Liu; Liyuan Jiang; Shuyan Zhang. 2020. "Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China." Remote Sensing 12, no. 1: 164.

Journal article
Published: 10 December 2019 in ISPRS International Journal of Geo-Information
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The heavy industry in India has witnessed rapid development in the past decades. This has increased the pressures and load on the Indian environment, and has also had a great impact on the world economy. In this study, the Preparatory Project Visible Infrared Imaging Radiometer (NPP VIIRS) 375-m active fire product (VNP14IMG) and night-time light (NTL) data were used to study the spatiotemporal patterns of heavy industrial development in India. We employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-term VNP14IMG data and artificial heat-source objects. Next, the initial heavy industry heat sources were distinguished from normal heat sources using a threshold recognition model. Finally, the maximum night-time light data were used to delineate the final heavy industry heat sources. The results suggest, that this modified method is a much more accurate and effective way of monitoring heavy industrial heat sources, and the accuracy of this detection model was higher than 92.7%. The number of main findings were concluded from the study: (1) the heavy industry heat sources are mainly concentrated in the north-east Assam state, east-central Jharkhand state, north Chhattisgarh and Odisha states, and the coastal areas of Gujarat and Maharashtra. Many heavy industrial heat sources were also found around a line from Kolkata on the Eastern Indian Ocean to Mumbai on the Western Indian Ocean. (2) The number of working heavy industry heat sources (NWH) and, particularly, the total number of fire hotspots for each working heavy industry heat source area (NFHWH) are continuing to increase in India. These trends mirror those for the Gross Domestic Product (GDP) and total population of India between 2012 and 2017. (3) The largest values of NWH and NFHWH were in Jharkhand, Chhattisgarh, and Odisha whereas the smallest negative values, the S l o p e _ N W H in Jharkhand and Chhattisgarh were also the two largest values in the whole country. The smallest negative values of S l o p e _ N W H and S l o p e _ N F H W H were in Haryana. The S l o p e _ N F H W H in the mainland Gujarat had the second most negative value, while the value of the S l o p e _ N W H was the third-highest positive value.

ACS Style

Caihong Ma; Zheng Niu; Yan Ma; Fu Chen; Jin Yang; Jianbo Liu. Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018. ISPRS International Journal of Geo-Information 2019, 8, 568 .

AMA Style

Caihong Ma, Zheng Niu, Yan Ma, Fu Chen, Jin Yang, Jianbo Liu. Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018. ISPRS International Journal of Geo-Information. 2019; 8 (12):568.

Chicago/Turabian Style

Caihong Ma; Zheng Niu; Yan Ma; Fu Chen; Jin Yang; Jianbo Liu. 2019. "Assessing the Distribution of Heavy Industrial Heat Sources in India between 2012 and 2018." ISPRS International Journal of Geo-Information 8, no. 12: 568.

Journal article
Published: 28 October 2019 in Remote Sensing
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Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and Encoder-Decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the Encoder stage. The details and spatial dimensions of targets are restored using such information during the Decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and Encoder-Decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.

ACS Style

Wei Xia; Caihong Ma; Jianbo Liu; Shibin Liu; Fu Chen; Zhi Yang; Jianbo Duan. High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks. Remote Sensing 2019, 11, 2523 .

AMA Style

Wei Xia, Caihong Ma, Jianbo Liu, Shibin Liu, Fu Chen, Zhi Yang, Jianbo Duan. High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks. Remote Sensing. 2019; 11 (21):2523.

Chicago/Turabian Style

Wei Xia; Caihong Ma; Jianbo Liu; Shibin Liu; Fu Chen; Zhi Yang; Jianbo Duan. 2019. "High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks." Remote Sensing 11, no. 21: 2523.

Journal article
Published: 26 November 2018 in Sustainability
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Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.

ACS Style

Caihong Ma; Jin Yang; Fu Chen; Yan Ma; Jianbo Liu; Xinpeng Li; Jianbo Duan; Rui Guo. Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data. Sustainability 2018, 10, 4419 .

AMA Style

Caihong Ma, Jin Yang, Fu Chen, Yan Ma, Jianbo Liu, Xinpeng Li, Jianbo Duan, Rui Guo. Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data. Sustainability. 2018; 10 (12):4419.

Chicago/Turabian Style

Caihong Ma; Jin Yang; Fu Chen; Yan Ma; Jianbo Liu; Xinpeng Li; Jianbo Duan; Rui Guo. 2018. "Assessing Heavy Industrial Heat Source Distribution in China Using Real-Time VIIRS Active Fire/Hotspot Data." Sustainability 10, no. 12: 4419.

Journal article
Published: 26 March 2018 in Remote Sensing
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Quarry sites result from human activity, which includes the removal of original vegetation and the overlying soil to dig out stones for building use. Therefore, the dynamics of the quarry area provide a unique view of human mining activities. Actually, the topographic changes caused by mining activities are also a result of the development of the local economy. Thus, monitoring the quarry area can provide information about the policies of the economy and environmental protection. In this paper, we developed a combined method of machine learning classification and quarry region analysis to estimate the quarry area in a quarry region near Beijing. A temporal smoothing based on the classification results of all years was applied in post-processing to remove outliers and obtain gently changing sequences along the monitoring term. The method was applied to Landsat images to derive a quarry distribution map and quarry area time series from 1984 to 2017, revealing significant inter-annual variability. The time series revealed a five-stage development of the quarry area with different growth patterns. As the study region lies on two jurisdictions—Tianjin and Hebei—a comparison of the quarry area changes in the two jurisdictions was applied, which revealed that the different policies in the two regions could impose different impacts on the development of a quarry area. An analysis concerning the relationship between quarry area and gross regional product (GRP) was performed to explore the potential application on socioeconomic studies, and we found a strong positive correlation between quarry area and GRP in Langfang City, Hebei Province. These results demonstrate the potential benefit of annual monitoring over the long-term for socioeconomic studies, which can be used for mining decision making.

ACS Style

Haoteng Zhao; Yong Ma; Fu Chen; Jianbo Liu; Liyuan Jiang; Wutao Yao; Jin Yang. Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study. Remote Sensing 2018, 10, 517 .

AMA Style

Haoteng Zhao, Yong Ma, Fu Chen, Jianbo Liu, Liyuan Jiang, Wutao Yao, Jin Yang. Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study. Remote Sensing. 2018; 10 (4):517.

Chicago/Turabian Style

Haoteng Zhao; Yong Ma; Fu Chen; Jianbo Liu; Liyuan Jiang; Wutao Yao; Jin Yang. 2018. "Monitoring Quarry Area with Landsat Long Time-Series for Socioeconomic Study." Remote Sensing 10, no. 4: 517.

Journal article
Published: 14 March 2018 in ISPRS International Journal of Geo-Information
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Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

ACS Style

Rui Guo; Jianbo Liu; Na Li; Shibin Liu; Fu Chen; Bo Cheng; Jianbo Duan; Xinpeng Li; Caihong Ma. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks. ISPRS International Journal of Geo-Information 2018, 7, 110 .

AMA Style

Rui Guo, Jianbo Liu, Na Li, Shibin Liu, Fu Chen, Bo Cheng, Jianbo Duan, Xinpeng Li, Caihong Ma. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks. ISPRS International Journal of Geo-Information. 2018; 7 (3):110.

Chicago/Turabian Style

Rui Guo; Jianbo Liu; Na Li; Shibin Liu; Fu Chen; Bo Cheng; Jianbo Duan; Xinpeng Li; Caihong Ma. 2018. "Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks." ISPRS International Journal of Geo-Information 7, no. 3: 110.

Journal article
Published: 18 October 2017 in ISPRS International Journal of Geo-Information
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With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images.

ACS Style

Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. ISPRS International Journal of Geo-Information 2017, 6, 310 .

AMA Style

Caihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan, Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. ISPRS International Journal of Geo-Information. 2017; 6 (10):310.

Chicago/Turabian Style

Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. 2017. "A Content-Based Remote Sensing Image Change Information Retrieval Model." ISPRS International Journal of Geo-Information 6, no. 10: 310.

Preprint
Published: 29 August 2017
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With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

ACS Style

Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. 2017, 1 .

AMA Style

Caihong Ma, Wei Xia, Fu Chen, Jianbo Liu, Qin Dai, Liyuan Jiang, Jianbo Duan, Wei Liu. A Content-Based Remote Sensing Image Change Information Retrieval Model. . 2017; ():1.

Chicago/Turabian Style

Caihong Ma; Wei Xia; Fu Chen; Jianbo Liu; Qin Dai; Liyuan Jiang; Jianbo Duan; Wei Liu. 2017. "A Content-Based Remote Sensing Image Change Information Retrieval Model." , no. : 1.

Journal article
Published: 26 March 2016 in Remote Sensing
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Disaster change mapping, which can provide accurate and timely changed information (e.g., damaged buildings, accessibility of road and the shelter sites) for decision makers to guide and support a plan for coordinating emergency rescue, is critical for early disaster rescue. In this paper, we focus on optical remote sensing data to propose an automatic procedure to reduce the impacts of optical data limitations and provide the emergency information in the early phases of a disaster. The procedure utilizes a series of new methods, such as an Optimizable Variational Model (OptVM) for image fusion and a scale-invariant feature transform (SIFT) constraint optical flow method (SIFT-OFM) for image registration, to produce product maps including cloudless backdrop maps and change-detection maps for catastrophic event regions, helping people to be aware of the whole scope of the disaster and assess the distribution and magnitude of damage. These product maps have a rather high accuracy as they are based on high precision preprocessing results in spectral consistency and geometric, which compared with traditional fused and registration methods by visual qualitative or quantitative analysis. The procedure is fully automated without any manual intervention to save response time. It also can be applied to many situations.

ACS Style

Yong Ma; Fu Chen; Jianbo Liu; Yang He; Jianbo Duan; Xinpeng Li. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing. Remote Sensing 2016, 8, 272 .

AMA Style

Yong Ma, Fu Chen, Jianbo Liu, Yang He, Jianbo Duan, Xinpeng Li. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing. Remote Sensing. 2016; 8 (4):272.

Chicago/Turabian Style

Yong Ma; Fu Chen; Jianbo Liu; Yang He; Jianbo Duan; Xinpeng Li. 2016. "An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing." Remote Sensing 8, no. 4: 272.