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Received：01 November 2023，
Published：07 December 2023
移动端阅览
遥感图像有向目标检测是一项非常有挑战性的任务，受到了广泛的关注。随着深度学习的迅速发展，基于卷积神经网络（CNN）和自注意力网络（Transformer）的神经网络在有向目标检测方面取得了显著成果。然而，对于遥感图像中的有向目标，仍然存在对边界信息和显著特征信息的关注不足的问题。其中，不同方向目标的边界信息有限且难以提取，而显著特征的全局依赖关系相对稀疏。因此，本文提出了基于特征重组和自注意力的遥感图像有向目标检测方法。该方法主要包括空间通道重组的回归分支和自注意力分类分支。其中，回归分支通过在通道维度中重组空间信息，更加关注边界敏感信息，以实现对定位框的精确定位。分类分支依据带有位置信息的自注意力捕获目标根本判别性的特征，并增强特征的全局依赖性，从而实现准确分类。通过广泛的实验验证，证明了所提出模型的有效性和鲁棒性。在公开数据集DOTA、HRSC2016和SODA-A上表现优秀。
Oriented object detection in remote sensing images is an exceptionally challenging task that has elicited widespread attention. With the rapid advancement of deep learning
neural networks based on convolutional neural networks and self-attention networks (e.g.
Transformers) have achieved remarkable progress in oriented object detection. However
the focus on boundary and salient feature information in oriented objects in remote sensing images is lacking. Specifically
extracting boundary information for objects with varying orientations is difficult
and the global dependency of salient features is sparse. To address these issues
we propose a method of small-object detection in remote sensing images on the basis of feature reassembly and self-attention. This method consists of a regression branch that incorporates spatial channel reassembly and a self-attention classification branch. The regression branch reassembles spatial information along the channel dimension and emphasizes boundary-sensitive information to achieve accurate localization of bounding boxes. The classification branch leverages self-attention with positional information to capture fundamentally discriminative object features
thus enhancing global feature dependencies for precise classification. Extensive experiments demonstrate the effectiveness and robustness of the proposed model and showcase its excellent performance on publicly available datasets
such as DOTA
HRSC2016
and SODA-A.
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