Characterized by complicated backgrounds, various types, large size variations, and arbitrary orientations, the detection and recognition of arbitrary-oriented objects in remote sensing images are challenging. To address the aforementioned problem, an anchor-free arbitrary-oriented object detector using box boundary-aware vectors is proposed. With the idea of CenterNet to detect objects as points, oriented object detection is achieved by predicting the center, the box boundary-aware vectors, the size, and the type of the bounding box. In the feature extraction stage of the designed architecture, Res2Net, a multiscale convolutional neural network, is used to extract feature maps of different scales and adaptively spatial feature fusion is adopted to improve the detector's adaptability to objects of different sizes. In the detector, a context enhancement module with a multibranch network is designed to enhance the contextual information of the objects and improve the detector's robustness to the complicated backgrounds. Experiments are carried on three challenging benchmarks (i.e., HRSC2016, UCAS-AOD, and DOTA) and our method achieves state-of-the-art performance with 90.30%, 89.70%, and 77.18% mAP, respectively.

Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detection tasks. This study developed an adaptive feature-aware method of object detection in remote sensing images based on the single-shot detector architecture called adaptive feature-aware detector (AFADet). Self-attention is used to extract high-level semantic information derived from deep feature maps for spatial localization of objects and the model is improved in localizing objects. The adaptive feature-aware module is used to perform adaptive cross-scale depth fusion of different-scale feature maps to improve the learning ability of the model and reduce the influence of complex backgrounds in remote sensing images. The focal loss is used during training to address the positive and negative sample imbalance problem, reduce the influence of the loss value dominated by easily classified samples, and enhance the stability of model training. Experiments are conducted on three object detection datasets, and the results are compared with those of the classical and recent object detection algorithms. The mean average precision(mAP) values are 66.12%, 95.54%, and 86.44% for three datasets, which suggests that AFADet can detect remote sensing images in real-time with high accuracy and can effectively balance detection accuracy and speed.

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the detection task. To overcome these issues, we proposed a one-stage remote sensing image object detection model: a multi-feature information complementary detector (MFICDet). This detector contains a positive and negative feature guidance module (PNFG) and a global feature information complementary module (GFIC). Specifically, the PNFG is used to refine features that are beneficial for object detection and explore the noisy features in a complex background of abstract features. The proportion of beneficial features in the feature information stream is increased by suppressing noisy features. The GFIC uses pooling to compress the deep abstract features and improve the model’s ability to resist feature displacement and rotation. The pooling operation has the disadvantage of losing detailed feature information; thus, dilated convolution is introduced for feature complementation. Dilated convolution increases the receptive field of the model while maintaining an unchanged spatial resolution. This can improve the ability of the model to recognize long-distance dependent information and establish spatial location relationships between features. The detector proposed also improves the detection performance of objects at different scales in the same image using a dual multi-scale feature fusion strategy. Finally, classification and regression tasks are decoupled in space using a decoupled head. We experimented on the DIOR and NWPU VHR-10 datasets to demonstrate that the newly proposed MFICDet achieves competitive performance compared to current state-of-the-art detectors.

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP₇₅ values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.

The goal of cross-view image based geo-localization is to determine the location of a given street-view image by matching it with a collection of geo-tagged aerial images, which has important applications in the fields of remote sensing information utilization and augmented reality. Most current cross-view image based geo-localization methods focus on the image content and ignore the relations between feature nodes, resulting in insufficient mining of effective information. To address this problem, this study proposes feature relation guided cross-view image based geo-localization. This method first processes aerial remote sensing images using a polar transform to achieve the geometric coarse alignment of ground-to-aerial images, and then realizes local contextual feature concern and global feature correlation modeling of the images through the feature relation guided attention generation module designed in this study. Specifically, the module includes two branches of deformable convolution based multiscale contextual feature extraction and global spatial relations mining, which effectively capture global structural information between feature nodes at different locations while correlating contextual features and guiding global feature attention generation. Finally, a novel feature aggregation module, MixVPR, is introduced to aggregate global feature descriptors to accomplish image matching and localization. After experimental validation, the cross-view image based geo-localization algorithm proposed in this study yields results of 92.08%, 97.70%, and 98.66% for the top 1, top 5, and top 10 metrics, respectively, in CVUSA, a popular public cross-view dataset, and exhibits superior performance compared to algorithms of the same type.

Compared with general object detection, the scale variations, arbitrary orientations, and complex backgrounds of objects in remote sensing images make it more challenging to detect oriented objects. Especially for oriented objects that have large aspect ratios, it is more difficult to accurately detect their boundary. Many methods show excellent performance on oriented object detection, most of which are anchor-based algorithms. To mitigate the performance gap between anchor-free algorithms and anchor-based algorithms, this paper proposes an anchor-free algorithm called dual-resolution and deformable multi-head network (DDMNet) for oriented object detection. Specifically, the dual-resolution network with bilateral fusion is adopted to extract high-resolution feature maps which contain both spatial details and multi-scale contextual information. Then, the deformable convolution is incorporated into the network to alleviate the misalignment problem of oriented object detection. And a dilated feature fusion module is performed on the deformable feature maps to expand their receptive fields. Finally, box boundary-aware vectors instead of the angle are leveraged to represent the oriented bounding box and the multi-head network is designed to get robust predictions. DDMNet is a single-stage oriented object detection method without using anchors and exhibits promising performance on the public challenging benchmarks. DDMNet obtains 90.49%, 93.25%, and 78.66% mean average precision (mAP) on the HRSC2016, FGSD2021, and DOTA datasets. In particular, DDMNet achieves 79.86% at mAP ₇₅ and 53.85% at mAP ₈₅ on the HRSC2016 dataset, respectively, outperforming the current state-of-the-art methods.