Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received long-standing attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this article aims to present a comprehensive review of the recent achievements in deep learning-based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multiscale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.

Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedicated to designing Transformer-based architectures for spatial-spectral long-range dependencies modeling, which is computationally expensive with quadratic complexity. Selective structured state space model (Mamba), which is efficient for modeling long-range dependencies with linear complexity, has recently shown promising progress. However, its potential in hyperspectral image processing that requires handling numerous spectral bands has not yet been explored. In this paper, we innovatively propose S2Mamba, a spatial-spectral state space model for hyperspectral image classification, to excavate spatial-spectral contextual features, resulting in more efficient and accurate land cover analysis. In S2Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion. More specifically, S2Mamba first captures spatial contextual relations by interacting each pixel with its adjacent through a Patch Cross Scanning module and then explores semantic information from continuous spectral bands through a Bi-directional Spectral Scanning module. Considering the distinct expertise of the two attributes in homogenous and complicated texture scenes, we realize the Spatial-spectral Mixture Gate by a group of learnable matrices, allowing for the adaptive incorporation of representations learned across different dimensions. Extensive experiments conducted on HSI classification benchmarks demonstrate the superiority and prospect of S2Mamba. The code will be made available at: https://github.com/PURE-melo/S2Mamba.

With the rapid advances in deep learning techniques, remote sensing object detection has achieved remarkable achievements in recent years. However, tiny object detection remains unsatisfactory and suffers from two main drawbacks, including (1) the high sensitivity of IoU for location deviation in tiny objects and (2) the poor-quality feature representations of tiny objects. To address the aforementioned problems, we propose a Multi-stage Enhancement Network (MENet) that achieves the instance-level and feature-level enhancement of tiny objects from different stages of the detector. Since the IoU-based label assignment drastically deteriorates the positive samples for tiny objects, we first propose a Central Region-based (CR) label assignment to substitute it in the Region Proposal Network (RPN). The CR label assignment regards the anchors that fall into the central region of ground-truth boxes as positive samples, which provides more positive samples for tiny objects. Then, we design a Gated Context Aggregation (GCA) module that selectively aggregates valuable context information to enhance the feature representation of tiny objects. Additionally, we devise a positive RoI feature (pRoI) generator in the Region Convolutional Neural Network (R-CNN) to generate a rich diversity of high-quality positive RoI features for tiny objects. We conduct extensive experiments on AI-TOD and SODA-A datasets, and the results demonstrate the effectiveness of our proposed method.

Ship detection is a significant and challenging task in remote sensing. Due to the arbitrary-oriented property and large aspect ratio of ships, most of the existing detectors adopt rotation boxes to represent ships. However, manual-designed rotation anchors are needed in these detectors, which causes multiplied computational cost and inaccurate box regression. To address the abovementioned problems, an anchor-free rotation ship detector, named GRS-Det, is proposed, which mainly consists of a feature extraction network with selective concatenation module (SCM), a rotation Gaussian-Mask model, and a fully convolutional network-based detection module. First, a U-shape network with SCM is used to extract multiscale feature maps. With the help of SCM, the channel unbalance problem between different-level features in feature fusion is solved. Then, a rotation Gaussian-Mask is designed to model the ship based on its geometry characteristics, which aims at solving the mislabeling problem of rotation bounding boxes. Meanwhile, the Gaussian-Mask leverages context information to strengthen the perception of ships. Finally, multiscale feature maps are fed to the detection module for classification and regression of each pixel. Our proposed method, evaluated on ship detection benchmarks, including HRSC2016 and DOTA Ship data sets, achieves state-of-the-art results.

Hyperspectral image change detection (HSI-CD) aims to detect subtle changes on the Earth’s surface through approximately continuous spectral information, which has gradually become a very important research hotspot in the field of remote sensing (RS). In recent years, convolutional neural networks (CNNs) based HSI-CD methods have shown strong feature extraction capabilities. However, due to the simple fusion of spectral information in the channel dimension by CNN, the medium and long-term sequence properties of spectral features cannot be well mined and represented. Most previous studies mainly extract semantic features from images at different times, ignoring the temporal correlation between features, which cannot fully extract and effectively utilize temporal-spatial-spectral features. To this end, this paper proposes a cascade spectral aware transformer (CAST) for HSI-CD. First, we propose a temporal-spatial transformer (TS-Former) to enhance the temporal correlation and spatial global relationship of extracted features, thereby addressing the insufficient consideration of temporal correlation. Second, a spectral awareness transformer (SA-Former) is designed to better mine and represent the sequence properties of spectral features, especially the medium and long-term dependencies. Finally, we observe a spectral distortion in the process of extracting temporal-spatial features and based on this present a spectral constraint module (SCM) to preserve the sequence properties of spectral features and reduce the distortion of the spectrum. Extensive experiments on three challenging hyperspectral datasets demonstrate that our method achieves state-of-the-art results. The code is available at: https://github.com/tianshunli/CAST.