An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

Chein I. Chang, Hsuan Ren

Research output: Contribution to journalArticlepeer-review

117 Scopus citations

Abstract

Over the past years, many algorithms have been developed for multispectral and hyperspectral image classification. A general approach to mixed pixel classification is linear spectral unmixing, which uses a linear mixture model to estimate the abundance fractions of signatures within a mixed pixel. As a result, the images generated for classification are usually gray scale images, where the gray level value of a pixel represents a combined amount of the abundance of spectral signatures residing in this pixel. Due to a lack of standardized data, these mixed pixel algorithms have not been rigorously compared using a unified framework. In this paper, we present a comparative study of some popular classification algorithms through a standardized HYDICE data set with a custom-designed detection and classification criterion. The algorithms to be considered for this study are those developed for spectral unmixing, the orthogonal subspace projection (OSP), maximum likelihood, minimum distance, and Fisher's linear discriminant analysis (LDA). In order to compare mixed pixel classification algorithms against pure pixel classification algorithms, the mixed pixels are converted to pure ones by a designed mixed-to-pure pixel converter. The standardized HYDICE data are then used to evaluate the performance of various pure and mixed pixel classification algorithms. Since all targets in the HYDICE image scenes can be spatially located to pixel level, the experimental results can be presented by tallies of the number of targets detected and classified for quantitative analysis.

Original languageEnglish
Pages (from-to)1044-1063
Number of pages20
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume38
Issue number2 II
DOIs
StatePublished - Mar 2000

Keywords

  • Linear discriminant analysis (LDA)
  • Linear unmixing
  • Maximum likelihood estimator (MLE)

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