With the continuous development of space remote sensing technology, the spatial resolution of visible remote sensing images has been continuously improved, which has promoted the progress of remote sensing target detection. However, due to the limitation of sensor lattice size, it is still challenging to obtain a large range of high-resolution (HR) remote sensing images in practical applications, which makes it difficult to carry out target monitoring in a large range of areas. At present, many object detection methods focus on the detection and positioning technology of HR remote sensing images, but there are relatively few studies on object detection methods using medium- and low-resolution (M-LR) remote sensing images. Because of its wide coverage area and short observation period, M-LR remote sensing imagery is of great significance for obtaining information quickly in space applications. However, the small amount of fine-texture information on objects in M-LR images brings great challenges to detection and recognition tasks. Therefore, we propose a small target detection method based on degradation reconstruction, named DRADNet. Different from the previous methods that use super resolution as a pre-processing step and then directly input the image into the detector, we have designed an additional degenerate reconstruction-assisted framework to effectively improve the detector’s performance in detection tasks with M-LR remote sensing images. In addition, we introduce a hybrid parallel-attention feature fusion module in the detector to achieve focused attention on target features and suppress redundant complex backgrounds, thus improving the accuracy of the model in small target localization. The experimental results are based on the widely used VEDAI dataset and Airbus-Ships dataset, and verify the effectiveness of our method in the detection of small- and medium-sized targets in M-LR remote sensing images.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online.

Object detection in remote sensing images is an important research topic in the field of remote sensing intelligent interpretation. Although modern object detectors have made good progress, high-precision oriented object detection still faces severe challenges due to the large-scale variation, strong directional diversity and complex background interference of objects in remote sensing images. Currently, most remote sensing object detectors focus on modeling object characteristics in the spatial domain while ignoring the frequency domain information of the object. Recent studies have shown that frequency domain learning has brought substantial benefits in many visual fields. To this end, we proposed an adaptive dual-domain dynamic interaction network (AD³I-Net) for oriented object detection tasks in remote sensing images. The network has three important components: a spatial adaptive selection (SAS) module, a frequency adaptive selection (FAS) module, and a dual-domain feature interaction (DDFI) module. The SAS module adaptively models spatial context information and dynamically adjusts the feature receptive field to construct more accurate spatial position features for objects of different scales. The FAS module uses the transformation from the spatial domain to the frequency domain to adaptively learn the frequency information of the object, to model direction features, and to make up for the lack of spatial domain information. Finally, through the DDFI module, the features extracted from the two domains are interactively fused, thereby bridging the complementary information to enhance the feature expression of the object and give it rich spatial position and direction information. The AD³I-Net we proposed fully exploits the interaction between the different domains and improves the model’s ability to capture subtle object features. Our method has been extensively experimentally verified on two mainstream datasets, HRSC2016 and DIOR-R. The experimental results demonstrate that this method performs competitively in oriented object detection tasks.

This study mainly addresses the issues of an excessive model parameter count and computational complexity in Detection Transformer (DETR) for remote sensing object detection and similar neural networks. We propose an innovative neural network pruning method called “ant colony evolutionary pruning (ACEP)” which reduces the number of parameters in the neural network to improve the performance and efficiency of DETR-based neural networks in the remote sensing field. To retain the original network’s performance as much as possible, we combine population evolution and ant colony algorithms for dynamic search processes to automatically find efficient sparse sub-networks. Additionally, we design three different sparse operators based on the structural characteristics of DETR-like neural networks. Furthermore, considering the characteristics of remote sensing objects, we introduce sparsity constraints to each network layer to achieve efficient network pruning. The experimental results demonstrate that ACEP is effective on various DETR-like models. After removing a significant number of redundant parameters, it greatly improves the inference speed of these networks when performing remote sensing object detection tasks.