Recent studies in remote sensing object detection have made excellent progress and shown promising performance. However, most current detectors only explore rotation-invariant feature extraction but disregard the valuable spatial and semantic prior knowledge in remote sensing images (RSIs), which limits the detection performance when encountering blurred or heavy occluded objects. To address this issue, we propose a mask-reconstruction relation learning (MRRL) framework to learn such prior knowledge among objects and a consistency-reasoning transformer over relation proposals (CTRP) to recognize objects with limited visual features via consistency reasoning. Specifically, MRRL framework applies random mask to some objects in the training dataset and performs masked objects reconstruction to guide the network to learn the distribution consistency of objects. CTRP is the core component of the MRRL framework, which models the interaction between spatial and semantic priors, and uses easy detected objects to reason hard detected objects. The trained CTRP can be integrated into the existing detector to improve the ability of object detection with limited visual features in RSIs. Extensive experiments on widely-used datasets for two distinct tasks, namely remote sensing object detection task and occluded object detection task, demonstrate the effectiveness of the proposed method. Source code is available at https://github.com/sunpeng96/CTRP_mmrotate.

Oriented object detection has attained remarkable progress in addressing the challenges associated with rotating invariant feature extraction. However, most existing object detection most existing encounter serious performance degradation when processing objects with tiny size, blurring and occlusion. One of the reasons is that existing methods mainly focus on local features, while overlooking the correlation among objects and common sense knowledge, which is inconsistent with the human visual system. To address this issue, we propose a dual-stream global relationship learning (DGRL) method consisting of two modules: a dynamic correlation learning (DCL) method and a global knowledge mapping (GKM) module. The DCL method can construct a region-to-region dynamic relationship graph based on feature correlations and implicitly guides the detection network to learn more powerful class representation by updating nodes of the graph. The GKM module generates a class-to-class global semantic relationship graph via external knowledge and achieve more stable representation learning by dynamically global relational mapping. Extensive experiments are performed on oriented object detection datasets DOTA, HRSC2016, DIOR-R as well as horizontal object detection datasets NWPU VHR-10, RSOD. The results demonstrate that the proposed method achieves the state-of-the-art detection accuracy.

Deep learning-based template matching in remote sensing has received increasing research attention. Existing anchor box-based and anchor-free methods often suffer from low template localization accuracy in the presence of multimodal, nonrigid deformation and occlusion. To address this problem, we transform the template matching task into a center-point localization task for the first time and propose an end-to-end template matching method based on a novel fully convolutional Siamese network. Furthermore, we propose an adaptive shrinkage cross-correlation scheme, which improves the precision of template localization and alleviates the impact of background clutter without adding any parameters. We also design a scheme that leverages keypoint information to assist in locating the template center, thereby enhancing the precision of template localization. We construct a multimodal template matching dataset to verify the performance of the method in dealing with differences in view, scale, rotation and occlusion in practical application scenarios. Extensive experiments on a public dataset, OTB, the proposed dataset, as well as a remote sensing dataset, SEN1-2, demonstrate that our method achieves state-of-the-art performance.