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Caihong Ma
Sanya Institute of Remote Sensing, Sanya 572029, China

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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: 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: 14 September 2016 in Remote Sensing
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Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

ACS Style

Shuai Xie; Jianbo Duan; Shibin Liu; Qin Dai; Wei Liu; Yong Ma; Rui Guo; Caihong Ma. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing 2016, 8, 759 .

AMA Style

Shuai Xie, Jianbo Duan, Shibin Liu, Qin Dai, Wei Liu, Yong Ma, Rui Guo, Caihong Ma. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sensing. 2016; 8 (9):759.

Chicago/Turabian Style

Shuai Xie; Jianbo Duan; Shibin Liu; Qin Dai; Wei Liu; Yong Ma; Rui Guo; Caihong Ma. 2016. "Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake." Remote Sensing 8, no. 9: 759.