Integration of Contextual Knowledge in Unsupervised Subpixel Classification: Semivariogram and Pixel-Affinity Based Approaches

P. V. Arun, K. M. Buddhiraju, A. Porwal

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This letter investigates the use of coarse-image features for predicting class labels at a given finer spatial scale. In this regard, two unsupervised subpixel mapping approaches, a semivariogram method, and a pixel-affinity based method are proposed. Furthermore, segmentation-based spectral unmixing is explored so as to address the spectral variability and nonconvexity of classes. In addition, the gradient information is employed to resolve uncertainties in the unmixing process. The proposed modifications based on pixel-affinity and semivariogram have produced an accuracy improvement of 5% or more over the state-of-the-art approaches.

Original languageEnglish
Article number8248645
Pages (from-to)262-266
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume15
Issue number2
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

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