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.

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.

In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.

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.

Recently, remote sensing image object detection based on convolutional neural networks (CNNs) has made significant advancements. However, small objects detection remains a major challenge in this field. Because the small size of the object makes it difficult to extract their features and these features are further weakened after downsampling in the network. In order to improve the detection accuracy of small objects in remote sensing images, this letter provides a feature super-resolution fusion framework based on cross-scale distillation. Specifically, we design a sub-pixel super-resolution feature pyramid network (SSRFPN) replacing the bilinear interpolation with sub-pixel super-resolution (SSR) modules to enhance the feature expression capability. Furthermore, we propose a cross-scale distillation (CSD) mechanism to guide the SSR modules in learning the features of small object regions more accurately. Finally, our method is applied to three detectors on two datasets for validation. We adopt YOLOv7 as the baseline model and achieve the best results, with the average precision at a threshold of 0.5 (AP0.5) of 95.0% and 82.3% on the NWPU VHR-10 dateset and DIOR dataset. And the mean average precision of small objects (mAPS) is improved by 8.5% and 2.5%.

In the field of remote sensing image (RSI) object detection, the oriented box annotation can accurately locate remote sensing objects with arbitrary orientation and obtain object orientation information. However, the detection based on oriented bounding box (OBB) is still a challenging task, and the detection effect needs to be improved. In RSI, the distribution of objects is extremely uneven, and the situation of object aggregation is easy to occur. Some researchers believe that the characteristics of dense distribution of objects are one of the reasons for the difficulty of object detection. However, the impact of dense distribution on detection performance has not been studied in depth. To address this problem, this paper proposes a dense object determination method based on oriented box annotation, which determines the dense objects in the dataset by two conditions consisting of inter-class distance, intra-class distance, minimum distance between objects, and minimum edge length of objects. The analysis of the experimental results of dense and non-dense object detection concludes that the characteristics of dense distribution in RSI do not easily cause the objects to be more difficult to detect. In order to make full use of the object features, this paper proposes a second stage detection head named region of interest fusion network (RoIF-Net), in which we extract region of interest (RoI) from the input image and fuse it with the RoI extracted from the feature maps to add detail features, and construct a feature induction module based on a self-attentive mechanism to achieve position regression and category classification. This structure can be used in any two-stage detection network to enhance detection capabilities. Using our method on three credible and challenging datasets, DOTA, DIOR-R and UCAS-AOD, we obtained 81.80%, 68.49% and 90.25% mAP results respectively, reaching SOTA based on OBB detection on these datasets, proving the effectiveness and advancement of our method.