Detecting objects in remote sensing images using oriented bounding boxes is flourishing but challenging, wherein the design of oriented bounding box representations is the key to achieving accurate detection. In this paper, we focus on two issues that hinder the performance of two-stage oriented detectors: (i) the notorious boundary discontinuity problem, which would result in significant loss increases in boundary conditions, and (ii) the inconsistency in regression schemes between the two stages. We propose a simple and effective bounding box representation by drawing inspiration from the polar coordinate system and integrate it into both two detection stages to circumvent the two issues. The first stage specifically initializes four quadrant points as the starting points of the regression for producing high-quality oriented candidates without any post-processing. In the second stage, the final localization results are refined using the proposed novel bounding box representation, which can fully release the capabilities of the oriented detectors. Such consistency brings a good trade-off between accuracy and speed. With only flipping augmentation and single-scale training and testing, our approach with ResNet-50-FPN harvests 76.25% mAP on the DOTA dataset with a speed of up to 16.5 FPS, achieving the best accuracy and the fastest speed among mainstream two-stage oriented detectors. Additional results on the DIOR-R and HRSC2016 datasets also demonstrate the effectiveness and robustness of our method. The source code will be publicly available at https://github.com/yanqingyao1994/QPDet.

Practical applications ask for object detection models that achieve high performance at low overhead. Knowledge distillation demonstrates favorable potential in this case by transferring knowledge from a cumbersome teacher model to a lightweight student model. However, previous distillation methods are plagued with massive misleading background information in remote sensing images and ignore investigating the relationships between different instances. In this article, we propose an instance-aware distillation (InsDist for short) method to derive efficient remote sensing object detectors. Our InsDist combines feature-based and relation-based knowledge distillation to make the most of instance-related information in the knowledge transfer from the teacher to the student. On one hand, we propose a parameter-free masking module to decouple instance-related foreground from instance-irrelevant background in multiscale features. On the other hand, we construct the relationships between different instances to enhance the learning of intraclass compactness and interclass dispersion. The student comprehensively imitates both features and relationships from the teacher, yielding considerable effectiveness in dealing with complex remote sensing images. In addition, our InsDist can be easily built on mainstream object detectors with negligible extra cost. Extensive experiments on two large-scale remote sensing object detection datasets, namely DIOR and DOTA, show that our InsDist obtains noticeable gains over other distillation methods for both one-stage and two-stage, as well as both anchor-based and anchor-free detectors. The source code will be publicly available at https://github.com/swift1988/InsDist.

This article studies change detection within pairs of optical images remotely sensed from overhead views. We consider that a high-performance solution to this task entails highly effective multi-level feature interaction. With that in mind, we propose a novel approach characterized by two attentive feature aggregation schemes that handle cross-level features in different processes. For the Siamese-based feature extraction of the bi-temporal image pair, we attach emphasis on constructing semantically strong and contextually rich pyramidal feature representations to enable comprehensive matching and differencing. To this end, we leverage a feature pyramid network and re-formulate its cross-level feature merging procedure as top-down modulation with multiplicative channel attention and additive gated attention. For the multi-level difference feature fusion, we progressively fuse the derived difference feature pyramid in an attend-then-filter manner. This makes the high-level fused features and the adjacent lower-level difference features constrain each other, and thus allows steady feature fusion for specifying change regions. In addition, we build an upsampling head as a replacement for the normal heads followed by static upsampling. Our implementation contains a stack of upsampling modules that allocate features for each pixel. Each has a learnable branch that produces attentive residuals for refining the statically upsampled results. We conduct extensive experiments on four public datasets and results show that our approach achieves state-of-the-art performance. Code is available at https://github.com/xingronaldo/CLAFA.

Infrared small target detection is considerably challenging due to the few pixels in targets, low signal-to-noise ratio, and complex background. In this paper, we propose an effective Attention-guided Feature Enhancement Network (AFE-Net) which can leverage the local and non-local features of targets and background in infrared images. The AFE-Net consists of three key modules, namely encoder and decoder interactive guidance (EDIG) module, cascading false alarm removal (CFAR) module, and random scale input (RSI) module. Specifically, in the EDIG module, we employ a channel attention mechanism on encoding and decoding layers to select feature channels with higher contribution. Then, we impose a bottom-up point-wise attention block to highlight the features of small infrared targets and suppress possible noise by incorporating the low-level detailed features into the high-level semantic features. The CFAR module extracts affluent global features by cascading non-local operations of different layers, which can remove clutters with similar features to infrared targets. The RSI module is placed in front of the entire detection network to extract multi-scale features of infrared small targets, which can enhance the robustness of the proposed network. Experimental results on the SIRST dataset and comprehensive comparisons with representative methods demonstrate the superiority of our proposed method.

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.