%0 Journal Article %1 noauthororeditor %A Keerthana, P. %A Sivasankar, A. %D 2013 %E Kumar, Dr. Shiv %J International Journal of Innovative Science and Modern Engineering (IJISME) %K (SVM) Hyperspectral Principal adaptive analysis component compression data end extraction lossy machine member spectral support transform unmixing vector wavelet %N 7 %P 35-37 %T The Impact of Lossy Compression on Hyperspectral Data Adaptive Spectral Unmixing and PCA Classification %U https://www.ijisme.org/wp-content/uploads/papers/v1i7/G0340061713.pdf %V 1 %X In the past, scientific data have been almost exclusively compressed by means of lossless methods, in order to preserve their full quality. However, more recently, there has been an increasing interest in the lossy compression which has not yet globally accepted by the remote sensing community, mainly because it is sensed that the lossy compressed images may affect the results of posterior processing stages. Hence here, the influence of lossy compression on two standard approaches for hyperspectral data exploitation known as adaptive spectral unmixing, and supervised classification using PCA are considered. The experimental result states that the adaptive spectral unmixing provides a user defined spatial scale which improves the process of extraction of end members and PCA improves the classification accuracy. It is also observed that, for certain compression techniques, a higher compression ratio may lead to more accurate classification results. This work further provides recommendations on best practices when applying lossy compression prior to hyperspectral data classification and/or unmixing.

@article{noauthororeditor, abstract = {In the past, scientific data have been almost exclusively compressed by means of lossless methods, in order to preserve their full quality. However, more recently, there has been an increasing interest in the lossy compression which has not yet globally accepted by the remote sensing community, mainly because it is sensed that the lossy compressed images may affect the results of posterior processing stages. Hence here, the influence of lossy compression on two standard approaches for hyperspectral data exploitation known as adaptive spectral unmixing, and supervised classification using PCA are considered. The experimental result states that the adaptive spectral unmixing provides a user defined spatial scale which improves the process of extraction of end members and PCA improves the classification accuracy. It is also observed that, for certain compression techniques, a higher compression ratio may lead to more accurate classification results. This work further provides recommendations on best practices when applying lossy compression prior to hyperspectral data classification and/or unmixing.}, added-at = {2021-09-23T09:43:42.000+0200}, author = {Keerthana, P. and Sivasankar, A.}, biburl = {https://www.bibsonomy.org/bibtex/209aab52c949b95693ca7c774d71a0347/ijisme_beiesp}, editor = {Kumar, Dr. Shiv}, interhash = {c7d44661632e354b28365b64592854dc}, intrahash = {09aab52c949b95693ca7c774d71a0347}, issn = {2319-6386}, journal = {International Journal of Innovative Science and Modern Engineering (IJISME)}, keywords = {(SVM) Hyperspectral Principal adaptive analysis component compression data end extraction lossy machine member spectral support transform unmixing vector wavelet}, language = {En}, month = {June}, number = 7, pages = {35-37}, timestamp = {2021-09-23T09:43:42.000+0200}, title = {The Impact of Lossy Compression on Hyperspectral Data Adaptive Spectral Unmixing and PCA Classification}, url = {https://www.ijisme.org/wp-content/uploads/papers/v1i7/G0340061713.pdf}, volume = 1, year = 2013 }