Spectral and Spatial Classification of Hyperspectral Images Based on ICA and Reduced Morphological Attribute Profiles

Falco, Nicola; Benediktsson, Jon Atli; Bruzzone, Lorenzo

doi:10.1109/tgrs.2015.2436335

Title: Spectral and Spatial Classification of Hyperspectral Images Based on ICA and Reduced Morphological Attribute Profiles

Journal Article · Thu Jul 02 00:00:00 EDT 2015 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2015.2436335· OSTI ID:1580041

Falco, Nicola ^[1]; Benediktsson, Jon Atli ^[1]; Bruzzone, Lorenzo ^[2]

Univ. of Iceland, Reykjavik (Iceland)
Univ. of Trento (Italy)

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. In this paper, a novel methodology that combines spectral and spatial information for supervised hyperspectral image classification is proposed. A feature reduction strategy based on independent component analysis is the main core of the spectral analysis, where the exploitation of prior information coupled to the evaluation of the reconstruction error assures the identification of the best class-informative subset of independent components. Reduced attribute profiles (APs), which are designed to address well-known issues related to information redundancy that affect the common morphological APs, are then employed for the modeling and fusion of the contextual information. Four real hyperspectral data sets, which are characterized by different spectral and spatial resolutions with a variety of scene typologies (urban, agriculture areas), have been used for assessing the accuracy and generalization capabilities of the proposed methodology. The obtained results demonstrate the classification effectiveness of the proposed approach in all different scene typologies, with respect to other state-of-the-art techniques.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1580041

Journal Information:: IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, Issue 11; ISSN 0196-2892

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 73 works

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A new Spectral–Spatial Jointed Hyperspectral Image Classification Approach Based on Fractal Dimension Analysis Su, Junying; Li, Yingkui; Hu, Qingwu Fractals, Vol. 27, Issue 05 https://doi.org/10.1142/s0218348x19500798	journal	August 2019
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