Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

Remote Sensing

Wenping Ma¹  Xiangrong Zhang¹  Can Qin²  Qiguang Miao²  Guifeng Mu²  Yue Wu² 

[1] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China;School of Computer Science and Technology, Xidian University, Xi’an 710071, China;

关键词: hyperspectral images;    semantic constraints;    spatial constrain;    self-training;   

DOI  :  10.3390/rs12010159

来源: DOAJ

【 摘 要 】

Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.

【 授权许可】

Unknown