Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

Xiaochen Lu,Tong Li,Junping Zhang,Ye Zhang

doi:10.3390/rs8100804

Abstract

Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, thereby improving the classification accuracy under the condition of small samples. This paper presents an improved synergetic classification scheme based on the concept of self-learning for HS and PAN images. The investigated scheme considers three basic rules, namely the identity rule, the uncertainty rule, and the diversity rule. By integrating the diversity of samples into the SL scheme, a more stable classifier is trained by using fewer samples. Experiments on three synthetic and real HS and PAN images reveal that the diversity criterion can avoid the problem of bias sampling, and has a certain advantage over the primary self-learning approach.

Highlights

Among all the remote sensing techniques, hyperspectral (HS) imaging is probably the most widely researched and applied one in earth observation, due to its powerful ability to recognize diverse land-covers and allow for the accurate analyses of terrestrial features
The SBSL-SPA stands for the spatial Euclidean distance–based diversity criterion, and SBSL-KCA/SBSL-KKM stand for kernel cosine angle and kernel k-means clustering, respectively
The diversity problem between unlabeled samples is addressed in the framework of the self-learning strategy, which is developed for the synergetic classification of hyperspectral and panchromatic images, to further enhance the classification accuracy and stability

Summary

Introduction

Among all the remote sensing techniques, hyperspectral (HS) imaging is probably the most widely researched and applied one in earth observation, due to its powerful ability to recognize diverse land-covers and allow for the accurate analyses of terrestrial features. Non-availability of a sufficient number of labeled samples is a general problem in pattern classification, and this is a more severe problem in remote sensing because it is extremely difficult and expensive to identify and label the samples, and sometimes it is not even feasible [3]. This observation has facilitated the idea of exploiting unlabeled samples to improve the capability of the classifiers. Semi-supervised algorithms incorporate the unlabeled samples and labeled samples to find a classifier with better boundaries [7,8]. A survey of SSL algorithms is available in [13]

Results

Discussion

Conclusion