Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

This work presents a robust graph mapping approach for the unsupervised heterogeneous change detection problem in remote sensing imagery. To address the challenge that heterogeneous images cannot be directly compared due to different imaging mechanisms, we take advantage of the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. The proposed method first constructs a robust K-nearest neighbor graph to represent the structure of each image, and then compares the graphs within the same image domain by means of graph mapping to calculate the forward and backward difference images, which can avoid the confusion of heterogeneous data. Finally, it detects the changes through a Markovian co-segmentation model that can fuse the forward and backward difference images in the segmentation process, which can be solved by the co-graph cut. Once the changed areas are detected by the Markovian co-segmentation, they will be propagated back into the graph construction process to reduce the influence of changed neighbors. This iterative framework makes the graph more robust and thus improves the final detection performance. Experimental results on different data sets confirm the effectiveness of the proposed method. Source code of the proposed method is made available at https://github.com/yulisun/IRG-McS.

Change detection (CD) of remote sensing (RS) images is one of the important problems in earth observation, which has been extensively studied in recent years. However, with the development of RS technology, the specific characteristics of remotely sensed images, including sensor characteristics, resolutions, noises, and distortions in imagery, make the CD more complex. In this article, we propose a structure consistency-based method for CD, which detects changes by comparing the structures of two images, rather than comparing the pixel values of images. Because the image structure is imaging modality-invariant and not sensitive to noise, illumination, and other interference factors, the proposed method can be applied to a variety of CD scenarios and has strong robustness. Structural comparison is realized by constructing and mapping an improved nonlocal patch-based graph (NLPG) to avoid the data leakage of two images. First, we demonstrate the effectiveness of the method in homogeneous and heterogeneous CD, which shows that the proposed method can be used as a unified CD framework. Second, we extend the method to the heterogeneous CD with multichannel synthetic aperture radar (SAR) image, which can provide a reference for future research as the heterogeneous CD with multichannel SAR is rarely studied. Third, through the decomposition and in-depth analysis of NLPG, we modify the graph construction process, structure difference calculation, and the difference image fusion to make it more robust and accurate. Experiments on six scenarios 12 data sets demonstrate the effectiveness of the proposed method.

Vehicle detection with orientation estimation in aerial images has received widespread interest as it is important for intelligent traffic management. This is a challenging task, not only because of the complex background and relatively small size of the target, but also the various orientations of vehicles in aerial images captured from the top view. The existing methods for oriented vehicle detection need several post-processing steps to generate final detection results with orientation, which are not efficient enough. Moreover, they can only get discrete orientation information for each target. In this paper, we present an end-to-end single convolutional neural network to generate arbitrarily-oriented detection results directly. Our approach, named Oriented_SSD (Single Shot MultiBox Detector, SSD), uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes. Meanwhile, offsets are predicted for each default box to better match the object shape, which contain the angle parameter for oriented bounding boxes’ generation. Evaluation results on the public DLR Vehicle Aerial dataset and Vehicle Detection in Aerial Imagery (VEDAI) dataset demonstrate that our method can detect both the location and orientation of the vehicle with high accuracy and fast speed. For test images in the DLR Vehicle Aerial dataset with a size of $5616\times 3744$ , our method achieves 76.1% average precision (AP) and 78.7% correct direction classification at 5.17 s on an NVIDIA GTX-1060.

With the advent of the era of high-resolution remote sensing, semantic segmentation methods for solving pixel-level classification have been widely studied. Deep learning has significantly advanced deep feature extraction methods, becoming widely employed in remote sensing image analysis. Deep feature fusion methods is able to effectively combine features from different sources. Optical and synthetic aperture radar (SAR) images stand out as primary data sources in remote sensing, offering complementary and consistent information. Fusion of deep semantic features of optical and SAR images can alleviate the limitations of single-source images in application and improve semantic segmentation accuracy. Therefore, this article reviews the research on deep fusion of optical and SAR images in semantic segmentation tasks from four aspects. Firstly, we provide a summary of challenges and research methods pertinent to semantic segmentation of remote sensing images. Then the challenges and urgent needs of deep feature fusion of optical and SAR images are analyzed, and current research is summarized from the perspective of structural design by studying various feature fusion strategies. Additionally, the compilation and in-depth analysis of open-source optical and SAR datasets suitable for semantic segmentation are undertaken, serving as fundamental resources for future research endeavors. Finally, the article identifies the major challenges summarized from the literature review in this field, outlining expectations and potential future directions for researchers.