Affiliation:
1. School of Electronic Engineering, Heilongjiang University, Harbin 150080, China
Abstract
With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.
Funder
National Key R&D Program of China
Subject
Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering
Cited by
94 articles.
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