Analysis of Land Area in Satellite Images Using HYPERSPECTRAL Remote Sensing Concept

Hyperspectral imaging otherwise called imaging spectrometry is a spectral sensing technique where objects are captured using various optical bands in broad spectral range. There are plenty of spectral information available in hyperspectral images to analyze and distinguish spectrally unique materials that gives more factual and detailed information extraction. These are further categorized into various land cover areas using different algorithms .It is used to differentiate materials that are spectrally similar. These images are also used to classify the defining factor of each land cover area such as minerals, soil and vegetation type. When the hyperspectral images are remotely sensed they can be used to analyze the images in depth in various areas. This technology is considered as a reliable technology for detection and identification. Land cover contains ecological description about a ground. Using remote sensing concept, parameters such as area, crop state and yield can be considered. In today’s technology, land based hyperspectral imaging is gaining immense interest in applications such as food inspection, forensic science, military and medical surgery. This project mainly focuses on fundamentals of hyperspectral image analysis and its modern applications in agriculture. The land area has been separated in the images using k-means algorithm. It is a useful baseline for further researches in hyperspectral image analysis.