Affiliation:
1. School of Computer and Control Engineering YanTai University Yantai China
2. School of Physics and Electronic Information Yantai University Yantai China
3. School of Computer Science and Educational Technology Hainan Normal University Hainan China
Abstract
AbstractImage segmentation is pivotal for the understanding of high‐resolution remote sensing images (HRRS). However, because of the inherent uncertainties in remote sensing images and the highly complex resolution of HRRS, ambiguity often occurs among some geographic entities in the segmentation process, and the fine segmentation of HRRS is not considered sufficiently for most existing segmentation methods. Therefore, in this paper, the authors propose a new collaborative neural network structure called fuzzy deep learning conditional random field network (FCUnet) to solve the refined segmentation of HRRS. First, the authors design a fuzzy U‐Net classification network to obtain effective feature information, which introduces the fuzzy logic unit into the network to process the ambiguity and uncertainty of HRRS. Then, the authors introduce the conditional random field (CRF) at the end of FCUnet to optimize the image segmentation results. Finally, the authors validated the effectiveness and superiority of their approach on three data sets. The experiment results revealed that FCUnet had better refined segmentation performance and generalization ability than state‐of‐the‐art methods.
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software
Cited by
3 articles.
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