Abstract
Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.
Highlights
Object detection has always been a popular and important task in computer vision [1]
We present a Dual Attention Feature Enhancement (DAFE) module to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by reweighting the spatial and channel feature responses
We propose a new Feature Enhancement Network (FENet) by unifying DAFE and Context Feature Enhancement (CFE) into the famous object detection framework of Faster R-CNN
Summary
Object detection has always been a popular and important task in computer vision [1]. Remote sensing images vary largely in object scale and aspect ratios. This is due to the difference of the Ground Sampling Distance (GSD) of aerial and satellite sensors and as a result of intraclass variations. There exists unbalanced distribution of foreground objects and complex background information, especially in intricate landforms and urban scenarios. All of these issues pose great challenges for current state-of-theart natural image detection algorithms
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