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Li C.,Central China Normal University | Li C.,Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs | Xiong H.,Central China Normal University | Tao S.,Central China Normal University | Han Z.,Central China Normal University
Journal of Remote Sensing

The classical methods of two dimensional remote change detection are no longer working effectively in three dimensional change scenes, such as geographical disaster areas. Vegetation information extraction also generally requires near-infrared bands. Visible light images from 2010 and 2011 in Yingxiu are selected and vegetation and three dimensional terrain change detection methods are presented under conditions without near-infrared bands. The Digital Elevation Model (DEM) and Digital Ortho Map (DOM) in two periods are generated by DPGrid. Then, vegetation changes in DOM are detected by the CIE Lab and Otsu algorithms. This study proposes an adaptive threshold method of three dimensional change detection based on the theory of probability and statistics. After censored samples are selected, the threshold range of elevation change is obtained by the 3σ rule of Gaussian distribution. High risk areas of geological disasters are extracted under the condition of high probability confidence regions. Finally, the corresponding earthwork change quantities are estimated by discretized integral. Vegetation changes in two periods are successfully detected, and change areas are located on the edge of the canyon in floodplain. This study determines the elevation change threshold range of three dimensional change detection by calculation. The threshold values of elevation decrease and increase are 2.73 m and 2.43 m, respectively. Landslide high risk areas and two debris flow accumulation areas located along Minjiang River are successfully detected. The earthwork quantitative change of 10 landslide high risk areas and two debris flow accumulation areas are estimated. Results show that the proposed method is effective, feasible, and practical. This study not only elevates conventional two dimensional change detection into three dimensional space but also quantitatively estimates three dimensional terrain changes. Therefore, the proposed method can be applied to monitor dynamic remote sensing and evaluate geological disasters. Even without near-infrared band, the proposed method can still successfully detect vegetation changes. In this study, statistical methods are used to determine the elevation change threshold. In allusion to landslide disaster, elevation-significant reduction risk areas are extracted. The distribution of risk areas is consistent with other research results. With discretized integral calculation, this study explores the earthwork quantity estimation method based on DEM. This method breaks the assumption that the surface is continuous and gradual, and can quickly estimate earthwork quantity by DEM. This method also incurs some errors caused by limited data source. The quantitative analysis for the uncertainty of the error is the main research target in the future. The two DEMs are generated separately in two periods; thus, they have their own errors in three dimensional change detection. Errors may also be amplified in error propagation. In future studies, the geographic information system data from previous periods will be used as control information to generate a DEM for a new period. Doing so would reduce relative errors. A domain-specific model will be also used to analyze and evaluate geological disasters. ©, 2014, Science Press. All right reserved. Source

Li C.,Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs | Li C.,Central China Normal University | Wang H.,Central China Normal University | Li Q.,Chinese Academy of Sciences | Sun M.,Wuhan University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

3D change detection is implemented by new epoch remotely sensed stereo pair with old epoch GIS-aided data that can provide information as prior knowledge. Firstly, exterior orientation elements of stereo pair are solved by control data supported by old epoch GIS data (DOM and DLG). Secondly, on the basis of a plane sweep method, dense multi-view matching is realized by object reference aided by a DOM and DEM. Thirdly, 3D change detection is carried out by subtracting an old epoch DEM from a new epoch DEM generated by above dense image matching. Finally, for remotely sensed aerial images of Wenchuan, experimental results for 3D change detection are shown so as to verify our approach. Source

Wang X.-L.,National Disaster Reduction Center | Wang X.-L.,Satellite Disaster Reduction Application Center | Wang X.-L.,Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs | Liu L.-F.,National Disaster Reduction Center | And 7 more authors.
Guangxue Jingmi Gongcheng/Optics and Precision Engineering

This paper introduces the acquisition of global land optical images from 2010 by the CCD cameras on HJ-1A/B to research the global land coverage and land usage situations. The HJ-1A/B is the first optical constellation with a ground resolution of 30 m and the revisit ability of 48 h in our country. When the optical remote sensing images are acquired by the HJ-1A/B constellation, the ground lighting conditions, cloud cover, season, terrain features and other factors must be considered, especially the more factors such as the satellite capacity, ground receiving ability, global land distribution and cloud distribution should be taken account of because the optical satellite has been used. Through one year's work, effective images with the cloud less than 20% covered more than 85% of global land are obtained. It is China's first middle-high resolution global coverage optical remote-sensing image sets and has been become the main data sources for global scale remote-sensing research. The ideals and applications to acquire the global land optical images by the HJ-1A/B constellation can provide references for planning optical constellations and designing payloads. Source

Wei L.,Wuhan University | Wei L.,Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs | Zhong Y.,Wuhan University | Zhang L.,Wuhan University | Li P.,Wuhan University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University

Traditional remote sensing image change detection methods have difficulty in detecting complete change information based on a single-band. To solve this problem, the paper proposes a multi-band remote sensing image change detection method using a Markov Random Field, fusing all band change information. In the process of solving the MRF model parameters, the MoLC (method of Log-Cumulants) and EM (expectation-maximization) hybrid model is introduced for iterative calculation. Experimental results show that the detection accuracy of the proposed method is superior to the current change detection methods, and is stable. Source

Wang T.-T.,Nanjing University of Information Science and Technology | Wang T.-T.,Nanjing Normal University | Ke W.,Nanjing Normal University | Ke W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs | Sun C.,Nanjing Normal University
Tongxin Xuebao/Journal on Communications

A novel two-step dictionary learning (DL) framework was proposed to dynamically adjust the overcomplete basis (a. k. a. dictionary) for matching the changes of the RSS measurements, and then the sparse solution can better represent location estimations. Moreover, a modified re-weighting l1 norm minimization algorithm was proposed to improve reconstruction performance for sparse signals. The effectiveness of the proposed scheme is demonstrated by experimental results where the locations of targets can be obtained from noisy signals, even if the number of targets is not known a priori. Source

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