Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.

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.

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.

Detecting the damaged building regions is vital to humanitarian assistance and disaster recovery after a disaster. Deep-learning-techniques based on aerial and Unmanned Aerial Vehicle (UAV) images have been extensively applied in the literature to detect damaged building regions, which are approved to be effective methods for fast response actions and rescue work. However, most of the existing building damaged region detection methods only consider the extraction accuracy of damaged regions from aerial or UAV images, which are not real-time and can hardly meet the practical application of emergency response. To address this problem, a new real-time building damaged region detection based on improved YOLOv5 and adapted to an embedded system from UAV images is proposed, which is named as DB-YOLOv5. First, residual dilated convolution module(Res-DConv) is employed to extract the spatial features, which can increase the receptive field. Then, a feature fusion module(BDSCAM) is designed to enhance the expressive ability of object feature, which could improve the classification performance of detector. Finally, a Double-Head method, an integration system of fully connected and convolution head for bounding box regression and classification, executes the localization task. The proposed DB-YOLOv5 method was evaluated using post-disaster UAV images collected over Ludian, China in 2013 and Beichuan, China in 2008. We found that the experimental results demonstrate that the proposed method is high accuracy and efficient for building damaged region detection and assessment on the embedded system. This approach is robust and suitable for practical application in disaster scenarios.

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.