Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of image SR techniques based on DL and have made remarkable progress. However, no scholar has provided a comprehensive review of the field. As a response, in this paper we aim to supply a comprehensive summary of the DL-based SR techniques for HSI, including upsampling frameworks, upsampling methods, network design, loss functions, representative works with different strategies, and future directions, in which we design several sets of comparative experiments for the advantages and limitations of two-dimensional convolution and three-dimensional convolution in the field of HSI SR and analyze the experimental results in depth. In addition, the paper also briefly discusses the secondary foci such as common datasets, evaluation metrics, and traditional SR algorithms. To the best of our knowledge, this paper is the first review on DL-based HSI SR.

Object detection plays a crucial role in remote sensing due to the urgent demands of various applications, such as urban planning and environmental monitoring. Despite notable progress, current methods still struggle with detecting challenging small objects. At the object level, the limited pixel representation, blurred details, and background interference of small objects impose greater demands on feature extractors. At the network level, resource bias fails to provide adequate learning signals for these objects. In this paper, we propose a Sparse Transformer-based detector (STDet) to tackle these challenges. Specifically, we design a Local-to-Global Transformer network (LGFormer) to explore essential feature representations. The Local Transformer Block establishes correlations between tokens and their surrounding data, while the Global Transformer Block captures long-distance dependencies related to the objects. Meanwhile, we introduce a Scale-Balanced Label Assignment (SBLA) strategy that considers more samples to small objects. SBLA dynamically shifts the learning focus to easily overlooked objects and alleviates the issue of sample imbalance. Extensive experiments on three large-scale remote sensing datasets demonstrate the effectiveness of STDet and its superiority in small object detection.

Deep learning has made significant achievements in remote sensing object detection tasks. However, small weak objects located in complex scenes are still not effectively addressed. The lack of feature information and negligible contributions during the optimization stage are the main reasons. To solve the indicated issues, a novel remote-sensing object detection method is proposed in this letter. Firstly, the context feature integration module (CFIM) is designed to extract implicit clues co-occurring with the object to compensate for the lack of features in small weak objects. The receptive field expansive deformable convolution (RFConv) constructed in CFIM can adaptively adjust the information extraction range based on the object’s characteristics, thereby capturing suitable context features. Secondly, to make small weak objects competitive during the optimization process, we propose tailored optimization functions: the balanced sampling strategy (BSS) and the modulated loss function (MLF). BSS makes up for the sample deficiency of small weak objects by balancing sampling. More specifically, BSS dynamically mines potential positive samples from the ignored set in order to increase the chances of matching. MLF progressively adjusts the loss proportion of the object and pushes the detector to be more sensitive to small weak objects during the training stage. Our proposed method achieves 94.79% and 71.23% mAP on the NWPU VHR-10 and DIOR datasets, which also proves the effectiveness.

Graph convolutional networks (GCNs) can extract features of samples in non-Euclidean space, which can be used for hyperspectral image (HSI) classification in collaboration with convolutional neural networks (CNNs). The features of GCNs and CNNs are incompatible to a certain extent, and traditional graph convolution methods use a single-channel 2-D matrix to extract features. As a result, it is difficult to explore the relationships fully and flexibly between samples. To further exploit the potential of these two networks for collaborative extraction of HSI features, we propose a fusion framework based on multichannel GCNs and class-guided attention mechanism (MG2A). Specifically, a multichannel GCN (MGCN) module is designed for batchwise network training, where the adjacent matrix of each channel contains different information between samples. In addition, we develop a class-guided attention mechanism to adaptively fuse the features of multiple MGCN modules and learn the transformation process of the features. Finally, the features of CNNs and MGCN modules are fused at multiple layers through a fusion framework. Experimental results on four benchmark HSI datasets show that MG2A achieves better classification performance compared to other state-of-the-art methods.