DocumentCode :
2690557
Title :
Multivariate AR Model based Support Vector Machine for Multispectral Remote Sensing Image Classification
Author :
Ho, Pei-Gee Peter ; Chen, C.H.
Author_Institution :
ECE Dept., Univ. of Massachusetts Dartmouth, Dartmouth, MA
Volume :
4
fYear :
2008
fDate :
7-11 July 2008
Abstract :
Time series statistical models such as autoregressive moving average (ARMA) were considered useful in describing the texture and contextual information of an remote sensing image. To simplify the computation, we use a two-dimensional (2-D) autoregressive (AR) model instead. In our previous research, the 2-D univariate time series based imaging model was derived mathematically to extract the features for further terrain segmentations. The effectiveness of the model was demonstrated in region segmentation of a multispectral image of the Lake Mulargias region in Italy. Due to the nature of remote sensing images such as SAR (synthetic aperture radar) and TM (Thermal Mapper) which are mostly in multi-spectral image stack format, a 2-D Multivariate Vector AR (ARV) time series model with pixel vectors of multiple elements (i.e. 15 elements in the case of TM+SAR remote sensing) are examined. The 2-D system parameter matrix and white noise error covariance matrix are estimated for further classifications. To compute the time series ARV system parameter matrix and estimate the error covariance matrix efficiently, a new method based on modern numerical analysis is developed by introducing the Schur complement matrix, the QR (orthogonal, upper triangular) matrix and the Cholesky factorizations in the ARV model formulation. As for pixel classification, the powerful Support Vector Machine (SVM) kernel based learning machine is applied in conjunction with the 2-D time series ARV model. The SVM is particularly suitable for the high dimensional vector measurement as the "curse of dimensionality" problem is avoided.
Keywords :
geophysical signal processing; image segmentation; image texture; radar imaging; remote sensing; remote sensing by radar; support vector machines; terrain mapping; time series; 2D autoregressive model; 2D multivariate vector AR time series model; 2D system parameter matrix; 2D univariate time series based imaging; Cholesky factorization; Italy; Lake Mulargias region; QR matrix; SAR; SVM learning machine; Schur complement matrix; autoregressive moving average; contextual information; features extraction; high dimensional vector measurement; multispectral remote sensing image classification; multivariate AR model; pixel classification; region segmentation; remote sensing imaging; support vector machine; synthetic aperture radar; terrain segmentation; texture information; thermal mapper; time series statistical model; white noise error covariance matrix; Autoregressive processes; Context modeling; Covariance matrix; Image classification; Image segmentation; Multispectral imaging; Remote sensing; Support vector machine classification; Support vector machines; Two dimensional displays;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
Type :
conf
DOI :
10.1109/IGARSS.2008.4779694
Filename :
4779694
Link To Document :
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