Paper Title
Hyperspectral Image Compression using Huffman Coding and Prediction Technique

The advancements in the field of remote sensing and geographic information has led the way for continuous improvement in the resolution and precision of satellite imaging sensors. Hyperspectral imaging, also known as imaging spectroscopy, is a key element in remote sensing. Significant constraints limiting the performance of instruments like hyper spectral sensors are the available transmission bandwidth and the on-board storage capacity. The compression step, thus, becomes a crucial part of the acquisition system as it enhances the easiness to store, access and transmit information. Ideally, the compression should be lossless to ensure preservation of the scientific value of data. Hyperspectral images exhibit significant spectral, spatial correlation whose exploitation is crucial for compression. In this project less complex method for Hyperspectral image compression is implemented based on differential prediction. The proposed scheme consists of a difference coder, two predictors and a Huffman codec. The processing of the pixels varies depending on their position in the image. The resulting difference between the predicted values and the actual pixel values are encoded into variable-length codewords using the Huffman codebook. The performance of the proposed algorithm is evaluated on AVIRIS images. Keywords - Hyperspectral, Huffman, Pixel, Lossless, Compression