Object detection-oriented style transfer network for panchromatic remote sensing image

Abstract

A panchromatic image is a remote sensing image that is imaged at the entire visible light band. It often has the highest spatial resolution and is widely used in the fields of resource detection, urban planning, geographic information systems, military, and national defense etc. However, the feature of single-band imaging determines that panchromatic images are usually displayed in the form of grayscale and result in some detailed differences between distinct types of ground objects that are indecipherable. It causes many difficulties for object detection applications. To address this problem, an object detection-oriented style transfer network for panchromatic remote sensing image is designed. In full consideration of the actual requirements of object detection tasks for panchromatic remote sensing image, a style transfer network based on feature fusion is designed, where a style transfer model is trained to transfer the grayscale panchromatic image into the corresponding color style. Further data preprocessing and postprocessing operations are designed to improve the quality of the transferred images and thus prevent negative transfer. DOTA dataset is used to verify the performance of the proposed algorithm. Results show that after the style transfer, the accuracy of object detection on the panchromatic remote sensing images has been significantly improved.