Title :
Land cover image classification using adaptive sparse fusion classifier
Author :
Anwar, Abdul Rauf ; Menaka, D.
Author_Institution :
Noorul Islam Univ., Kumaracoil, India
Abstract :
The presence of large number of spectral bands in the remote sensing images results in difficulty of identifying various land cover regions. Land cover classification is one of the recent researches which find more application in satellite image processing. It is important to recognize different land classes from a multispectral satellite image as the raw image contains noises and less clarity. The work was done in three stages such as preprocessing, feature extraction and classification. Noise present in the images are removed using a non-local means filter in preprocessing. Gabor wavelet and GLCM (gray level co-occurrence matrix) techniques were compared for feature extraction where PCA uses better in dimension reduction. The proposed sparse classifier efficiently classifies the given multispectral satellite image. It identifies the scattering features of same group of textures in the image, produces better accuracy compared to other techniques.
Keywords :
Gabor filters; feature extraction; geophysical image processing; geophysical techniques; image classification; image denoising; image fusion; land cover; matrix algebra; principal component analysis; remote sensing; wavelet transforms; GLCM technique; Gabor wavelet; PCA; adaptive sparse fusion classifier; dimension reduction; feature extraction; gray level cooccurrence matrix technique; image noise removal; image preprocessing; image textures; land class recognition; land cover image classification; land cover region identification; multispectral satellite image; nonlocal means filter; raw image; remote sensing images; satellite image processing; scattering feature identification; sparse classifier; spectral band; Feature extraction; Gabor filters; Image classification; Image color analysis; Noise; Remote sensing; Satellites; GLCM; Gabor Wavelet; Principal Component analysis; Remote Sensing; Sparse Fusion classifier;
Conference_Titel :
Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on
Conference_Location :
Kanyakumari
Print_ISBN :
978-1-4799-4191-9
DOI :
10.1109/ICCICCT.2014.6993000