Object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizontal bounding boxes (HBBs). However, the regression-based OBB detection methods always suffer from ambiguity in the definition of learning targets, which will decrease the detection accuracy. In this paper, we provide a comprehensive analysis of OBB representations and cast the OBB regression as a pixel-level classification problem, which can largely eliminate the ambiguity. The predicted masks are subsequently used to generate OBBs. To handle huge scale changes of objects in aerial images, an Inception Lateral Connection Network (ILCN) is utilized to enhance the Feature Pyramid Network (FPN). Furthermore, a Semantic Attention Network (SAN) is adopted to provide the semantic feature, which can help distinguish the object of interest from the cluttered background effectively. Empirical studies show that the entire method is simple yet efficient. Experimental results on two widely used datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed method outperforms state-of-the-art methods.

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Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data.

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