Wenqing Feng

Detecting building façade damage from oblique aerial images using local symmetry feature and the Gini Index

ABSTRACT 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.

Detection of Damaged Rooftop Areas From High-Resolution Aerial Images Based on Visual Bag-of-Words Model

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.

Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images

The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine (SVM) classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used.

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

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. (Less)

A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images

ABSTRACT 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.

Detecting Facade Damage on Moderate Damaged Type From High-Resolution Oblique Aerial Images

Oblique aerial images provide a more comprehensive view for the geometric and texture information of both rooftop and façade 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, façade 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.