Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

Object detection in SAR images has always been a topic of great interest in the field of deep learning. Early works commonly focus on improving performance on convolutional neural network frameworks. More recent works continue this path and introduce the attention mechanisms of Transformers for better semantic interpretation. However, these methods fail to treat the Transformer itself as a detection framework and, therefore, lack the development of various details that contribute to the state-of-the-art performance of Transformers. In this work, we first base our work on a fully multi-scale Transformer-based detection framework, DETR (DEtection TRansformer) to utilize its superior detection performance. Secondly, to acquire rotation-related attributes for better representation of SAR objects, an Orientation Enhancement Module (OEM) is proposed to facilitate the enhancement of rotation characteristics. Then, to enable learning of more effective and discriminative representations of foreground objects and background noises, a contrastive-loss-based GRC Loss is proposed to preserve patterns of both categories. Moreover, to not restrict comparisons exclusively to maritime objects, we have also developed an open-source labeled vehicle dataset. Finally, we evaluate both detection performance and generalization ability on two well-known ship datasets and our vehicle dataset. We demonstrated our method’s superior performance and generalization ability on both datasets.

Target fine-grained classification has been the research hotspot in remote sensing image interpretation, which has received general attention in application fields. One challenge of the fine-grained classification task is to learn the most discriminative feature using the deep convolutional neural network (DCNN). At present, many works of fine-grained image classification obtain target features by optimizing the feature extraction and enhancement, which are not accurate enough in remote sensing images. In this paper, we propose an essential feature mining network (EFM-net for short) based on DCNN to address this issue. Its major motivation is to obtain the essential feature which is fine enough to distinguish between similar instances. The proposed pipeline includes the Miner for purifying the essential feature and the Refiner for data augmentation. These two modules can work in a mutually reinforcing way, and extract the essential feature of targets. We evaluate EFM-Net on two public fine-grained classification datasets in remote sensing, FGSC-23 and FGSCR-42, and our Aircraft-16. The results show that the proposed method consistently outperforms existing alternatives. We have released our source code in Github https://github.com/JACYI/EFM-Net-Pytorch.git.

Deep convolutional neural network (DCNN) can achieve ship detection mission on the high-resolution remote sensing images. However, the false alarms caused by the onshore ship-like objects may decrease the accuracy and feasibility of these DCNN-based detection frameworks. In our work, an end-to-end method, named as Scene Mask R-CNN, is proposed to reduce the onshore false alarms. The scene mask extraction network (SMEN), as a network branch for scene segmentation, is innovatively introduced into the detection framework. The non-target area is marked out by an inferenced scene mask which is used to assist the ship detection. Combining the feature map originated from feature extraction network (FEN) with the inferenced scene mask by using the edge probability weighted (EPW) merging method, the false candidate targets in the non-target area are excluded. This novel mechanism of DCNN-based ship detection not only maintains the detection accuracy, but also effectively suppresses the false alarms in the non-target area. Finally, the validity and accuracy of this method are verified on a ship dataset generated by the high-resolution optical remote sensing images.

Recent years have witnessed rapid development and remarkable achievements on deep learning object detection in remote sensing (RS) images. The growing improvement of the accuracy is inseparable from the increasingly complex deep convolutional neural network and the huge amount of sample data. However, the under-fitting neural network will damage the detection performance facing the difficulty of sample acquisition. Thus, it evolves into few-shot object detection (FSOD). In this article, we first briefly introduce the object detection task and its algorithms, to better understand the basic detection frameworks followed by FSOD. Then, FSOD design methods in RS images for three important aspects, such as sample, model, and learning strategy, are respectively discussed. In addition, some valuable research results of FSOD in computer vision field are also included. We advocate a wide research technique route, and some advice about feature enhancement and multi-modal fusion, semantics extraction and cross-domain mapping, fine-tune and meta-learning strategies, and so on, are provided. Based on our stated research route, a novel few-shot detector that focuses on contextual information is proposed. At the end of the paper, we summarize accuracy performance on experimental datasets to illustrate the achievements and shortcomings of the stated algorithms, and highlight the future opportunities and challenges of FSOD in RS image interpretation, in the hope of providing insights into future research.

Accurate prow detection (i.e., ship heading prediction) is important in many applications that rely on optical remote sensing imagery, such as track forecasting and maritime navigation. In recent years, many advanced methods based on deep convolution neural networks (DCNNs) have succeeded in detecting multidirectional ships. However, these methods are not effective at determining the prow orientation, primarily due to three limitations: weak adaptability to geometric transformations of ship targets, confusing the semantic information between the prow and other parts of ships, and the boundary discontinuity problem. To address these problems, we propose an omnidirectional prow detection network (OPD-Net) based on feature enhancement and an improved regression model. OPD-Net consists of a feature refinement network (FRN), a prow attention network (PAN), and a complex plane coordinates regression model (CPCRM). First, the FRN balances the low-level location information and high-level semantic information from multiscale feature maps and then fits various geometric transformations regarding ship targets through deformable blocks. Next, the PAN, which is based on supervised learning, is used to enhance the ship prow feature as well as suppress background noise, which improves the accuracy of ship heading predictions. Finally, the CPCRM is designed to effectively solve the boundary discontinuity problem and correctly achieve prow detection in arbitrary orientations. Experiments on optical remote sensing image data sets demonstrate the robustness and superiority of our method for prow detection. Moreover, our approach is also competitive when used only for ship detection.