An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery

• Published in: IEEE transactions on geoscience and remote sensing Vol. 46; no. 12; pp. 4173 - 4185
• Main Authors: Huang, Xin, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied geophysics; Bandwidth selection; Classification; Computer programs; Data mining; Derivatives; Earth sciences; Earth, ocean, space; Exact sciences and technology; Extraction; high spatial resolution; hyperspectral; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Image resolution; Imagery; Internal geophysics; mean shift (MS); Neural networks; Remote sensing; Spatial resolution; Studies; Support vector machines; Urban areas; experimental studies; algorithms; high spatial resolution; Bandwidth selection; software; object oriented; hyperspectral; remote sensing; urban areas; classification; extraction; imagery; spatial resolution; mean shift (MS); high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2008.2002577

In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to interpret the feature set. In order to employ MS for hyperspectral data effectively, a feature-extraction algorithm, nonnegative matrix factorization, is utilized to reduce the high-dimensional feature space. Furthermore, two bandwidth-selection algorithms are proposed for the MS procedure. One is based on the local structures, and the other exploits separability analysis. Experiments are conducted on two hyperspectral data sets, the DC Mall hyperspectral digital-imagery collection experiment and the Purdue campus hyperspectral mapper images. We evaluate and compare the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely, the derivative of the morphological profile. Experimental results show that the proposed MS-based analysis system is robust and obviously outperforms the other methods.