Abstract
Conventional remote sensing classification techniques model the data in each class with a multivariate Gaussian distribution. Inadequacy of such algorithms stems from Gaussian distribution assumption for the class-component densities, which is only an assumption rather than a demonstrable property of natural spectral classes. In this paper, we present an Independent Component Analysis (ICA) based approach for unsupervised classification of multi/hyperspectral imagery. ICA employed for a mixture model, estimates the data density in each class and models class distributions with non-Gaussian structure (i.e. leptokurtic or platykurtic p.d.f), formulating the ICA mixture model (ICAMM). It finds independent components and the mixing matrix for each class, using the extended information-maximization learning algorithm, and computes the class membership probabilities for each pixel. We apply the ICAMM for unsupervised classification of images from a multispectral sensor - Positive Systems Multi-Spectral Imager, and a hyperspectral sensor - Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Four feature extraction techniques: Principal Component Analysis, Segmented Principal Component Analysis, Orthogonal Subspace Projection and Projection Pursuit have been considered as a preprocessing step to reduce dimensionality of the hyperspectral data. The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of remotely sensed images.
Original language | English (US) |
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Pages (from-to) | 569-580 |
Number of pages | 12 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5093 |
DOIs | |
State | Published - 2003 |
Event | Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX - Orlando, FL, United States Duration: Apr 21 2003 → Apr 24 2003 |
Keywords
- Blind source separation (BSS)
- ICA mixture model (ICAMM)
- Independent component analysis (ICA)
- Multispectral and hyperspectral imagery
- Unsupervised classification
- Unsupervised feature extraction
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering