Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

Abstract

Accurate spatial information of farmland in small farms is very important to provide operable information to farmers, managers and decision makers. However, small farms have small area, irregular shape, and use a variety of planting crops, which makes their boundaries blurred, and the standard edge detection algorithm cannot accurately segment the farmland boundary. Therefore, the automatic delimitation of fields in small farms is a challenging task. Aiming at the above problems, this paper proposes an example segmentation method of Mask R-CNN based on dual attention mechanism feature pyramid network (DAFPN) to describe small farms. DAFPN is composed of two attention modules: spatial attention module (SPA) and channel attention module (CHA) to enhance its feature extraction ability. Spatial attention module (SPA) generates spatial attention map by using the spatial relationship of features, and generates information to be emphasized or suppressed in spatial location; The channel attention module (CHA) learns an adaptive channel merging method based on the attention mechanism. Our proposed DAFPN can be easily inserted into the existing FPN model. We have conducted extensive experimental analysis on very high resolution (VHR) satellite images based on the Mask R-CNN deep learning framework of DAFPN. The standard COCO dataset evaluation index and F1-score evaluation strategy are used to compare the algorithm. AP 50 , AP 75 and F1-score reach 82.86%, 55.51% and 70.90% respectively, which is 8.7%, 8.31% and 6.87% higher than Mask R-CNN respectively. Our results highlight the ability of Mask R-CNN based on DAFPN to accurately depict small farms in VHR satellite images, which lays a foundation for the automatic segmentation of small farms.