TY - JOUR
AB - International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-3, January 2020 Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements Gunasheela K S, H S Prasantha Abstract: Hyperspectral image compression using compressive information separately [7] or combination of spectral and sensing technique is very much important in the area of satellite spatial information. Gaussian i.i.d. matrix can be used as image compression because it can greatly en hance the measurement matrix to obtain spectral compressivesampling compression rate. The research work proposes a novel data [14]. Spectral correlation [8] or different prior information adaptable sparse reconstruction algorithm for the reconstruction can be used to reconstruct the HSIs. Various methodologies of hyperspectral images from compressive sensing like principal component analysis, Multi-hypothesis measurements. In the proposed algorithm, compressive sensing estimation [9] has been proposed for the reconstruction of technique is used for the compression of HSIs, where Gaussian HSIs. In general most of the techniques for i.i.d. matrix is used to generate compressive sensing measurements. The algorithm solves the optimization problem HSIreconstruction perform direct reconstruction of HSI, containing total variation regularization and data adaptable which may cause high complexity at the computational parameter terms. The regularization 
TI - Data Adaptable Sparse Reconstruction for Hyperspectral Image Recovery from Compressed Measurements
JF - Regular Issue
DO - 10.35940/ijitee.c9007.109320
DA - 2020-01-10
UR - https://www.deepdyve.com/lp/unpaywall/data-adaptable-sparse-reconstruction-for-hyperspectral-image-recovery-akCEL2MquL
DP - DeepDyve
ER -