Abstract

Object detection from remote sensing images serves as an important prerequisite to many applications. However, caused by scale and orientation variations, appearance and distribution diversities, occlusion and shadow contaminations, and complex environmental scenarios of the objects in remote sensing images, it brings great challenges to realize highly accurate recognition of geospatial objects. This paper proposes a novel one-stage anchor-free capsule network (OA-CapsNet) for detecting geospatial objects from remote sensing images. By employing a capsule feature pyramid network architecture as the backbone, a pyramid of high-quality, semantically strong feature representations are generated at multiple scales for object detection. Integrated with two types of capsule feature attention modules, the feature quality is further enhanced by emphasizing channel-wise informative features and class-specific spatial features. By designing a centreness-assisted one-stage anchor-free object detection strategy, the proposed OA-CapsNet performs effectively in recognizing arbitrarily-orientated and diverse-scale geospatial objects. Quantitative evaluations on two large remote sensing datasets show that a competitive overall accuracy with a precision, a recall, and an F_score of 0.9625, 0.9228, and 0.9423, respectively, is achieved. Comparative studies also confirm the feasibility and superiority of the proposed OA-CapsNet in geospatial object detection tasks.

摘要

从遥感图像中检测目标是许多应用的重要先决条件。然而，由于遥感图像中对象的尺度和方向变化、外观和分布多样性、遮挡和阴影污染以及复杂的环境场景，给实现地理空间对象的高精度识别带来了巨大的挑战。本文提出了一种新的单阶段无锚胶囊网络（OA-CapsNet），用于从遥感图像中检测地理空间对象。通过采用胶囊特征金字塔网络架构作为主干，在多个尺度上生成高质量、语义强的特征表示的金字塔，用于对象检测。集成了两种胶囊特征注意力模块，通过强调通道方面的信息特征和特定于类的空间特征，进一步提高了特征质量。通过设计中心性辅助的单阶段无锚对象检测策略，所提出的 OA-CapsNet 在识别任意方向和不同尺度的地理空间对象方面表现出色。对两个大型遥感数据集的定量评估表明，具有精确度、召回率和F_得分分别为 0.9625、0.9228 和 0.9423。比较研究还证实了所提出的 OA-CapsNet 在地理空间对象检测任务中的可行性和优越性。