A novel change detection (CD) method for very high-resolution images is proposed by integrating multi-scale features. First, a novel edge density matching index was designed, and the structural similarity of textures, including grey level co-occurrence matrix, Gaussian Markov random field, and Gabor features between bitemporal images, were extracted to measure changes. Then, an adaptive approach was proposed to select optimal textures based on the majority consistency between spectrum and textures. Afterward, all features were decomposed into multi-scale features and fused into initial CD maps using Dempster–Shafer evidence theory. Finally, advantage fusion was implemented to generate the final CD map by fusing initial CD maps to remove noise and preserve details. Experiments conducted on real SPOT 5 and simulated QuickBird datasets, which achieved the total error ratios of 8.74% and 2.50%, respectively, indicate the effectiveness of the proposed approach.
Base on the research for the displacement deformation and additional stress numerical simulations of the buildings in the mining active area,the paper set up the relationship formation between the displacement deformation of the buildings and the ground displacement deformation.The paper summarized the distribution change law of the additional foundation reversing force and the additional horizontal stress of the buildings.Wherever the building set at any location of the subsidence base of the ground,there is a max stress at the bottom of the building.To increase the anti deformation capacity of the building,it is important to reinforce the bottom foundation of the building.The softer of the foundation material,the additional stress of the building will be.A rational soften foundation could reduce the additional stress of the building in order to protect the purpose of the buildings in the mining active area.
Image matching is an outstanding issue because of the existing of geometric and radiometric distortion in stereo remote sensing images. Weighted α-shape (WαSH) local invariant features are tolerant to image rotation, scale change, affine deformation, illumination change, and blurring. However, since the number of WαSH features is small, it is difficult to get enough matches to estimate the satisfactory homography matrix or fundamental matrix. In addition, the WαSH detector is extremely sensitive to image noise because it is built on sampled edges. Considering the shortcomings of the WαSH detector, this paper improves the WαSH feature matching method based on the 2D discrete wavelet transform (2D-DWT). The method firstly performs 2D-DWT on the image, and then detects WαSH features on the transformed images. According to the methods of descriptor construction for WαSH features, three matching methods on the basis of wavelet transform WαSH features (WWF), improved wavelet transform WαSH features (IWWF), and layered IWWF (LIWWF) are distinguished with respect to the character of the sub-images. The experimental results on the dataset containing affine distortion, scale distortion, illumination change, and noise images, showed that the proposed methods acquired more matches and better stableness than WαSH. Experimentation on remote sensing images with less affine distortion and slight noise showed that the proposed methods obtained the correct matching rate greater than 90%. For images containing severe distortion, KAZE obtained a 35.71% correct matching rate, which is unacceptable for calculating the homography matrix, while IWWF achieved a 71.42% correct matching rate. IWWF was the only method that achieved the correct matching rate of no less than 50% for all four test stereo remote sensing image pairs and was the most stable compared to MSER, DWT-MSER, WαSH, DWT-WαSH, KAZE, WWF, and LIWWF.
Structural information, extracted by simulating the human visual system (HVS), is independent of viewing conditions and individual observers. Structural similarity (SSIM), a measure of similarity between two images, has been widely used in image quality assessment. Given the fact that the change detection techniques identify the changed area by the similarity of multi-temporal images, SSIM has significant prospect in change detection of synthetic aperture radar (SAR) images. However, the experimental results show that SSIM performs worse in change detection of multi-temporal SAR images. In this study, we first propose an advanced SSIM (ASSIM) based on a two-step assumption of extracting structural information and a visual attention measure (VAM) model. Then, we propose a novel approach based on ASSIM for change detection in SAR images. SSIM, ASSIM, and state-of-the-art methods are tested on two datasets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can acquire a better difference image than SSIM and other state-of-the-art methods, and improve the accuracy of change detection in SAR images effectively.
Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD.
Accurate monitoring of the developing process of a surface subsidence basin is the basis of building damage assessment and surface deformation prediction. In this paper, the Synthetic Aperture Radar (SAR) data of three different imaging geometries, TerraSAR, Radarsat-2, and Sentinel-1A, were exploited. Firstly, two-dimensional (2D) time-series deformation of the surface subsidence basin caused by 15,235 working face mining was obtained based on Multidimensional Small Baseline Subset (MSBAS) technology from 19 December 2015 to 5 March 2016. By comparing vertical deformation with levelling data, it is shown that the root-mean-square error of vertical deformation is 3.2 mm and the standard deviation is 1.9 mm when the ascending-descending track SAR data is available. Otherwise, the root-mean-square error of vertical deformation is 18.1 mm and the standard deviation is 11.6 mm. Because of the low precision of the north–south horizontal movement monitored by the SAR sensor, the vertical deformation acquired by MSBAS technology and the rules of the mining subsidence (horizontal movement is proportional to tilt) were combined to obtain the north–south horizontal movement which was proven to be reliable by comparing the 2D time-series deformation obtained by MSBAS technology. Then, the deformation of the railway in the surface subsidence basin was analysed based on the three-dimensional (3D) time-series deformation. The results show that the subsidence, tilt, and horizontal movement strongly influence the railway in the monitoring period, but will not affect the normal traffic. This experiment lays a technical foundation for preventing the occurrence of mining disasters and verifies the ability to monitor the deformation of buildings and structures by interferometry synthetic aperture radar technology.
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