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Hyperspectral Image Compression & Classification: A Survey

Tahereen Rizvi, B. Sucharitha, Nida Zia, Syed Diraar Ahmed


The applications of Hyper spectral images (HSI) are many, which include agriculture, food quality, remote sensing, medical diagnostics and safety assessment. Hyper spectral image analysis has been used for detecting contaminants and identifying defects in food. It also utilizes advanced software and hardware tools hence allowing users to diagnose and detect pathologies. In this paper an avant-garde investigation about hyper spectral image compression and classification techniques which can be used in various applications like broadcasting of television, remote sensing via satellite, storage and classification of medical images, pictures and documents has been made. Significant increase in multimedia products has created a need to enhance, extract, store and interpret the information received in the most effective manner. The size of a hyper spectral image comprises approximately 138.81 megabytes and hence requires large space for storage. Hence, hyper spectral image compression is of great importance as it reduces the data redundancy and also the hardware space required for storage. Hyper spectral image classification has gained great research attention due to the increasing demand of feature information extraction. This survey focuses on describing the recent advances in spectral–spatial classification of hyperspectral images and various recent advancements in compression techniques for input HSIs. 


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