Classifying different satellite remote sensing scenes is a very important subtask in the field of remote sensing image interpretation. With the recent development of convolutional neural networks (CNNs), remote sensing scene classification methods have continued to improve. However, the use of recognition methods based on CNNs is challenging because the background of remote sensing image scenes is complex and many small objects often appear in these scenes. In this letter, to improve the feature extraction and generalization abilities of deep neural networks so that they can learn more discriminative features, an enhanced attention module (EAM) was designed. Our proposed method achieved very competitive performance—94.29% accuracy on NWPU-RESISC45 and state-of-the-art performance on different remote sensing scene recognition data sets. The experimental results show that the proposed method can learn more discriminative features than state-of-the-art methods, and it can effectively improve the accuracy of scene classification for remote sensing images. Our code is available at https://github.com/williamzhao95/Pay-More-Attention .

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

With the continuous advancement of remote sensing observation technology, wide-area observation and high-resolution imaging make remote sensing images contain a large number of dense tiny objects. The detection of dense tiny objects is a very challenging task since these objects are with very low resolution and might stick together. Existing work lacks further exploration of the contextual scene information and inherent characteristics of dense tiny objects, which are crucial for performance improvement of dense tiny object detection. In this work, we propose a novel Scene Contextualized Detection Network (SCDNet) by decoupling scene contextual information through a dedicated scene classification sub-network, thereby enabling an enhanced exploration of the relationship between tiny objects and their surrounding environments. In particular, we design a lightweight scene context guided fusion module in SCDNet to incorporate scene context information around dense tiny objects more effectively. Moreover, we further develop the scene context guided foreground enhancement module to suppress the background information while enhancing the foreground information based on the scene information. In addition, this research field still lacks a large-scale benchmark dataset with dense tiny objects, which is crucial for the training and comprehensive evaluation of detection methods. To this end, we construct a large-scale dataset for dense tiny object detection. It contains 11,600 images with 1,019,800 instances, the average absolute size of objects is smaller than 13 pixels, and each image contains 88 objects on average. Extensive experiments are conducted on the proposed dataset, and the results demonstrate the superiority and effectiveness of SCDNet compared to existing methods. The dataset and evaluation code are available at https://github.com/mmic-lcl.

Despite much research progress in synthetic aperture radar (SAR) object detection, the performance of SAR object detection has encountered a bottleneck limited by the imaging mechanism of SAR. In this work, we investigate how to perform robust SAR object detection by distilling the category knowledge from optical images in the training stage. To this end, we propose a novel knowledge distillation method called Category-oriented Localization Distillation (CoLD), which employs the optical object detection network as the teacher to guide the SAR object detection network. To introduce the category prior knowledge of the teacher network in the localization knowledge transferring, a category-oriented partition module is designed in CoLD to decouple candidate bounding boxes into target and non-target ones according to the category information in optical images. Through box decoupling, the accuracy and efficiency of SAR object detection can be significantly improved. Moreover, an IoU-based weighting module is introduced in CoLD to guide the student network focusing more on high-quality candidate boxes by adaptively changing the weight of each candidate bounding box based on the corresponding IoU score in the teacher network. In addition, a unified benchmark dataset is created for the evaluation of optical information guided SAR object detection, which consists of 14,665 optical and SAR image pairs in the training set and 3,666 SAR images in the testing set. Extensive experiments on the dataset demonstrate the effectiveness of our CoLD against state-of-the-art methods. The dataset is available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.

Unmanned Aerial Vehicle (UAV) thermal-imaging has received much attention, but the insufficient image resolution caused by thermal imaging systems is still a crucial problem that limits the understanding of thermal UAV images. However, high-resolution visible images are relatively easy to access, and it is thus valuable for exploring useful information from visible image to assist thermal UAV image super-resolution (SR). In this paper, we propose a novel Multi-conditioned Guidance Network (MGNet) to effectively mine the information of visible images for thermal UAV image SR. High-resolution visible UAV images usually contain salient appearance, semantic and edge information, which plays a critical role in boosting the performance of thermal UAV image SR. Therefore, we design an effective multi-cue guidance module to leverage the appearance, edge and semantic cues from visible images to guide thermal UAV image SR. In addition, we build the first benchmark dataset for the task of thermal UAV image SR guided by visible images. It is collected by a multi-modal UAV platform and composes of 1025 pairs of manually aligned visible and thermal images. Extensive experiments on the built dataset show that our MGNet can effectively leverage useful information from visible images to improve the performance of thermal UAV image SR and perform well against several state-of-the-art methods. The dataset is available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.