Classification of Hyperspectral Image using Principal component and Independent Component Analysis
Journal Title: Journal of Advanced Research in Geo Sciences & Remote Sensing - Year 2017, Vol 4, Issue 3
Abstract
Hyperspectral remote sensing sensors have the ability to acquire images in many narrow spectral bands that are found in the electromagnetic spectrum from visible, near infrared, medium infrared to thermalinfrared. Hyperspectral sensors capture energy from in 200 bands or more which means that they continuously cover the reflecting spectrum for each pixel in the scene. Bands characteristic for these types of sensors arecontinuous and narrow, allowing an indepth examination of features and details on Earth which recorded with multispectral sensors would be lost. The benefits of Hyperion hyperspectral data to LULC mapping have been studied at sites over some areas of Nilgiris district. The purpose of this study is to analyze the classification of hyperspectral images using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Hyperspectral imagery provides the potential for more accurate and detailed information extraction than is possible with other types of remotely sensed data. The processing of hyperspectral images (Hyperion data) and reducing the redundant signatures through PCA. The PCA processed data will be analyzed for LULC mapping using the Visual Interpretation method over some parts of Nilgiris district. Further the ICA is performed to isolate the spectral signature and that processed image is also applied for LULC Mapping. Finally the two results will get compared to determine the Accuracy Assessment between PCA and ICA. The expected result is, ICA provide more accuracy than the PCA.
Authors and Affiliations
Ms. Pooja VS,
Keywords
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