A derivative-aided hyperspectral image analysis system for land-cover classification

Fuan Tsai, William D. Philpot

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


The large number of spectral bands in hyperspectral data seriously complicates their use for classification. Selection of a useful subset of bands or derived features (spectral ratios, differences, derivatives) is always desirable, strongly affects the accuracy of the classification, and is often a practical necessity to keep the processing speed and memory requirements under control. This paper examines one possible procedure for selecting spectral derivatives to improve supervised classification of hyperspectral images. The procedure is designed to identify derivative features that are more effective at separating target classes and then add them to a base subset of features for classification. The goal is to create the smallest set of features that will result in the best classification result. A key issue in this process is the interplay of the number of features and the size of the training data sets since classification accuracy declines if the dimensionality of the feature space is too large relative to the number of training samples.

Original languageEnglish
Pages (from-to)416-425
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2
StatePublished - Feb 2002


  • Computer-aided data analysis
  • Hyperspectral image analysis
  • Spectral derivative


Dive into the research topics of 'A derivative-aided hyperspectral image analysis system for land-cover classification'. Together they form a unique fingerprint.

Cite this