SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Top Cited Papers
- 18 May 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 7 (4), 736-740
- https://doi.org/10.1109/lgrs.2010.2047711
Abstract
The high number of spectral bands acquired by hyperspectral sensors increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. This letter presents a novel method for accurate spectral-spatial classification of hyperspectral images. The proposed technique consists of two steps. In the first step, a probabilistic support vector machine pixelwise classification of the hyperspectral image is applied. In the second step, spatial contextual information is used for refining the classification results obtained in the first step. This is achieved by means of a Markov random field regularization. Experimental results are presented for three hyperspectral airborne images and compared with those obtained by recently proposed advanced spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.Keywords
This publication has 15 references indexed in Scilit:
- Segmentation and classification of hyperspectral images using watershed transformationPattern Recognition, 2010
- Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown From Automatically Selected MarkersIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
- Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological ProfilesIEEE Transactions on Geoscience and Remote Sensing, 2008
- Segmentation and Classification of Hyperspectral Data using WatershedPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imageryRemote Sensing of Environment, 2006
- Kernel-based methods for hyperspectral image classificationIEEE Transactions on Geoscience and Remote Sensing, 2005
- A Markov random field model for classification of multisource satellite imageryIEEE Transactions on Geoscience and Remote Sensing, 1996
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1984
- Classification of Multispectral Image Data by Extraction and Classification of Homogeneous ObjectsIEEE Transactions on Geoscience Electronics, 1976
- Equation of State Calculations by Fast Computing MachinesThe Journal of Chemical Physics, 1953