Accurate Instance Segmentation for Remote Sensing Images via Adaptive and Dynamic Feature Learning

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-12, Vol.13 (23), p.4774
• Main Authors: Yang, Feng, Yuan, Xiangyue, Ran, Jie, Shu, Wenqiang, Zhao, Yue, Qin, Anyong, Gao, Chenqiang
• Format: Article
• Language: English
• Subjects: Classification; context attention; Datasets; Design; Earth surface; Image processing; Image resolution; Image segmentation; Instance segmentation; Learning; Machine learning; Modules; multi-scale fusion; Neural networks; object detection; Object recognition; Remote sensing; remote sensing images; sample selection; Semantics
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13234774

Instance segmentation for high-resolution remote sensing images (HRSIs) is a fundamental yet challenging task in earth observation, which aims at achieving instance-level location and pixel-level classification for instances of interest on the earth’s surface. The main difficulties come from the huge scale variation, arbitrary instance shapes, and numerous densely packed small objects in HRSIs. In this paper, we design an end-to-end multi-category instance segmentation network for HRSIs, where three new modules based on adaptive and dynamic feature learning are proposed to address the above issues. The cross-scale adaptive fusion (CSAF) module introduces a novel multi-scale feature fusion mechanism to enhance the capability of the model to detect and segment objects with noticeable size variation. To predict precise masks for the complex boundaries of remote sensing instances, we embed a context attention upsampling (CAU) kernel instead of deconvolution in the segmentation branch to aggregate contextual information for refined upsampling. Furthermore, we extend the general fixed positive and negative sample judgment threshold strategy into a dynamic sample selection (DSS) module to select more suitable positive and negative samples flexibly for densely packed instances. These three modules enable a better feature learning of the instance segmentation network. Extensive experiments are conducted on the iSAID and NWU VHR-10 instance segmentation datasets to validate the proposed method. Attributing to the three proposed modules, we have achieved 1.9% and 2.9% segmentation performance improvements on these two datasets compared with the baseline method and achieved the state-of-the-art performance.