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Pore structure characterization of deep-buried coals and its implications on methane adsorption capacity are of great significance for further understanding the adsorption mechanism of methane mole...

Building extraction has attracted considerable attention in the field of remote sensing image analysis. Fully convolutional network modelling is a recently developed technique that is capable of si...

This article presents a novel change detection (CD) approach for high-resolution remote-sensing images, which incorporates visual saliency and random forest (RF). First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for super-pixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, super-pixel-based CD is implemented by applying RF based on these samples. Experimental results on Quickbird, Ziyuan 3 (ZY3), and Gaofen 2 (GF2) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

This letter presents a novel flood detection approach using full polarimetric synthetic aperture radar (PolSAR) images based on a level set method considering prior geoinformation. The prior geoinformation includes information derived from vector data and topography data. The main approach accomplishes flood detection by the improved level set method, an active contour segmentation model, based on the classical Wishart distribution. Vector data are used to generate the zero initial level set curves. To investigate the separability between water and nonwater low–backscattering objects in PolSAR images, topography information is incorporated into the level set function as a constraint. Moreover, we introduce a piecewise statistical method to refine the result with the Kullback–Leibler divergence of circular polarization coherence. In addition, we design a new quantitative evaluation index to assess flood detection results. For validation, three real PolSAR images of flooded area are tested. The experimental results confirm the effectiveness of the proposed method.

Oblique aerial images provide a more comprehensive view for the geometric and texture information of both rooftop and facade of buildings, hence it is possible to precisely detect damage grading of building for a detailed and overall damage assessment after a disaster event. The detection of damaged to building facades can improve the accuracy of damage-type classification to support reconstruction after disaster events, especially in the case of moderate damaged buildings. In this paper, a novel approach for automatic detection of damaged facade based on local symmetry feature and the Gini Index using oblique aerial images is presented. First, facade is extracted from oblique images using three-dimensional texture mapping. Then, local symmetry points are detected in a sliding window, and we obtain the histogram bins of local symmetry points from vertical and horizontal direction. Finally, damaged and nondamaged of building facade are distinguished using Gini Index. An evaluation of experimental results, for a selected study site of the Beichuan earthquake ruins, Sichuan, China, show that this method is feasible and effective for detection of damaged facade.

The Classification of damaged building types is currently a relevant topic in disaster assessment and management, and the detection of damaged building facades is important to improve the classification accuracy. In this letter, a novel approach for automatic detection of damaged facade based on local symmetry features and the Gini Index using oblique aerial images is presented. First, local symmetry points are detected in a sliding window. Then, we obtain histogram bins of local symmetrical points in the vertical and horizontal directions. Finally, damaged and undamaged of building facades are distinguished using Gini Index. An evaluation of experimental result, for a selected Beichuan earthquake ruins study site, in Sichuan, China, shows that this method is feasible and effective for the detection of damaged facades.

Fully convolutional network (FCN) modeling is a recently developed technique that is capable of significantly enhancing building extraction accuracy; it is an important branch of deep learning and uses advanced state-of-the-art techniques, especially with regard to building segmentation. In this paper, we present an enhanced deep convolutional encoder-decoder (DCED) network that has been customized for building extraction through the application of superpixelbased conditional random fields (SCRFs). The improved DCED network, with symmetrical dense-shortcut connection structures, is employed to establish the encoders for automatic extraction of building features. Our network’s encoders and decoders are also symmetrical. To further reduce the occurrence of falsely segmented buildings, and to sharpen the buildings’ boundaries, an SCRF is added to the end of the improved DCED architecture. Experimental results indicate that the proposed approach exhibits competitive quantitative and qualitative performance, effectively alleviating the salt-and-pepper phenomenon and retaining the edge structures of buildings. Compared with other state-of-the-art methods, our method demonstrably achieves the optimal final accuracies.

The classification of damaged building types has received increasing attention in recent years. The detection of damaged rooftop areas is crucial to improve the accuracy of classification of building damaged types. In this letter, an approach for the automatic detection of damaged rooftops areas based on the visual bag-of-words (BoWs) model is presented. First, the building rooftop is segmented into different superpixel areas. Then, the visual BoWs model is employed to build semantic feature vectors for damaged or nondamaged parts of each superpixel area. Finally, damaged and nondamaged parts of rooftop superpixel areas are discriminated using support vector machine. An evaluation of experimental results, for a selected study site of the Beichuan earthquake ruins, Sichuan, China, shows that this method is feasible and effective for the detection of damaged rooftop areas.

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

In the process of object-oriented change detection, the determination of the optimal segmentation scale is directly related to the subsequent change information extraction and analysis. Aiming at this problem, this paper presents a novel object-level change detection method based on multi-scale segmentation and fusion. First of all, the fine to coarse segmentation is used to obtain initial objects of different sizes; then, according to the features of the objects, Change Vector Analysis is used to obtain the change detection results of various scales. Furthermore, in order to improve the accuracy of change detection, this paper introduces fuzzy fusion and two kinds of decision level fusion methods to get the results of multi-scale fusion. Based on these methods, experiments are done with SPOT5 multi-spectral remote sensing imagery. Compared with pixel-level change detection methods, the overall accuracy of our method has been improved by nearly 10%, and the experimental results prove the feasibility and effectiveness of the fusion strategies.

In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.