Publication

Abstract : Recent advancements in remote sensing technologies allow us to categorize a wide range of crop varieties from satellite data processing methodologies. More precisely the hyperspectral images gives accurate results in classification techniques because of its high spectral and spatial resolutions. This study employs the spectral signature concepts in the backend to analyze the process The dimension of hyperspectral images are usually high ,hence we implement linear unmixing algorithm with respect to Vertex component Analysis(VCA).The resulting abundance maps represents the spectral information of the data vector. These abundance maps help to analyze the spectral compatibility of each material. The standard spectral signature of each crop is available is USGS database which is taken as a reference for classification results. Importance has given to the spectral features of crops to identify each species accurately without considering the dimensionality issues.