Fusion of Spectral-Spatial Classifiers for Hyperspectral Image Classification

Shengwei Zhong, Shuhan Chen, Chein I. Chang, Ye Zhang

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

A spectral-spatial (SS) hyperspectral classifier generally implements a spectral classifier (SC) followed by a spatial filter (SF) for classification. This article develops a new approach to fusing multiple SC-SF classifiers for hyperspectral image classification (HSIC) as to improve classification performance. To accomplish this goal an iterative process is particularly designed to fuse the spatial-filtered classification maps (SFMaps) produced by each of SC-SF classifiers into one single SFMap via maximum a posteriori (MAP) criterion. Such fused SFMaps are then fed back and added to the current data cube to create a new data set for next round SC-SF classifier fusion. The same process is repeated iteratively until it satisfies an automatic stopping rule. To further fuse more than two SS methods, two approaches are also developed, called simultaneous multiple SC-SF fusion (SMSSF) method and progressive multiple SC-SF fusion (PMSSF) method. Experimental results demonstrate that fusing multiple SC-SF classifiers can indeed perform better than using an individual single SC-SF classifier alone without fusion.

Original languageEnglish
Article number9207854
Pages (from-to)5008-5027
Number of pages20
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume59
Issue number6
DOIs
Publication statusPublished - 2021 Jun

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences

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