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.

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.

High-resolution optical remote sensing data can be utilized to investigate the human behavior and the activities of artificial targets, for example ship detection on the sea. Recently, the deep convolutional neural network (DCNN) in the field of deep learning is widely used in image processing, especially in target detection tasks. Therefore, a complete processing system called the broad area target search (BATS) is proposed based on DCNN in this paper, which contains data import, processing and storage steps. In this system, aiming at the problem of onshore false alarms, a method named as Mask-Faster R-CNN is proposed to differentiate the target and non-target areas by introducing a semantic segmentation sub network into the Faster R-CNN. In addition, we propose a DCNN framework named as Saliency-Faster R-CNN to deal with the problem of multi-scale ships detection, which solves the problem of missing detection caused by the inconsistency between large-scale targets and training samples. Based on these DCNN-based methods, the BATS system is tested to verify that our system can integrate different ship detection methods to effectively solve the problems that existed in the ship detection task. Furthermore, our system provides an interface for users, as a data-driven learning, to optimize the DCNN-based methods.

Keypoint detection aims to automatically locate the most significant and informative points in remote sensing images, which directly affects the accuracy of matching and registration. In contrast to the hand-crafted keypoint detectors that heavily rely on the morphological gradient of corner, line, and ridge, the learning-based detectors emphasize obtaining reliable keypoints from deep features. However, the limited accuracy of semantics undermines the reliability of keypoints, especially in challenging scenarios characterized by repeated textures and boundaries. Therefore, a novel trustworthy semantic keypoint (TSK) detector is proposed for remote sensing images. It utilizes a lightweight multi-scale feature extraction and fusion network, along with a saliency keypoint localization mechanism, to facilitate keypoint detection. Notably, the TSK detector employed explicit semantics which is refined with multiple learning strategies about repeatability and representability across the multi-granularity reasoning spaces, namely pixel window, neighbor window, and existence entity. Finally, several metrics about repeatability, matching, and registration are used to evaluate the performance of the TSK detector and other competitive methods. Four remote sensing image datasets, including MICGE, HRSCD, OSCD, and SZTAKI, are used to verify performances. TSK detector achieves competitive performance against existing methods.

With the rapid development in the field of artificial intelligence, isolated object-level perception tasks have achieved tremendous success. However, in the field of object group understanding, existing remote sensing scene graph generation (SGG) methods, a structured description of objects and their inter relationships, tend to focus on comprehensive scenes with numerous elements and complex relationships, which may obscure the characterization of high-value relationships. Consequently, in specific scenes, the concise and efficient description of spatial-temporal relationships between high-value objects (such as vehicles and ships) presents a challenge. Simultaneously, the complexity of spatial relationships within the remote sensing object group necessitates the use of global context to aid in predicting these relationships, while fine-grained local context is essential for accurate relationship prediction. To address this, we propose a Ship Group Relationship Description method (SGRD) based on remote sensing SGG with a global and local context fusion network, called GLFN. The proposed network integrates global feature fusion through a Transformer-based self-attention mechanism and enhances local feature fusion using a graph convolutional network focused on object-specific graph structures. Additionally, we introduce a new dataset featuring Ship Group Scene Graphs, named SG2, derived and refined from the HRSC2016 dataset. Experimental results demonstrate that GLFN achieves competitive performance on SG2 in specific scenes compared to classical and recent SGG methods, offering potential for relationship-guided change detection and situational awareness. The constructed SG2 dataset will be made publicly available along with the paper https://github.com/SmallRqw/SG2.

Ship change detection is of great significance for maritime safety supervision, wharf vessel management, and vessel life cycle analysis. Nowadays, the ship change information is obtained through the difference between the multiple independent object detection results of multi-temporal images. However, it neglects the temporal correlation of the concerned features, impeding further improvement of the detection accuracy of change detection. Therefore, based on the sequential network, namely convolution LSTM, we built an end-to-end ship change detection (SCD) R-CNN network. The network extracts abstract semantic information reflecting the changed features of ships, and the time correlation between features of the different phases is established. Specifically, the changed features are utilized to guide the judgement of ship change. It is verified from the RS images that the proposed network avoids the misjudgment caused by the errors of object detection in the conventional method. In addition, a higher efficiency is revealed, maintaining the accuracy of the change detection of ship targets.