Improving execution time for supervised sparse representation classification of hyperspectral images using the Moore–Penrose pseudoinverse-Reference-Cited by-同舟云学术

Improving execution time for supervised sparse representation classification of hyperspectral images using the Moore–Penrose pseudoinverse

Published:2019-06-13 Issue:02 Volume:13 Page:1
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:
Short-container-title:J. Appl. Rem. Sens.

Author:

Arias Fernando X.¹,Sierra Heidy¹,Arzuaga Emmanuel¹

Affiliation:

1. Laboratory for Applied Remote Sensing, Imaging and Photonics, Mayaguez, Puerto Rico

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference27 articles.

1. Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the central regions of Iran;Yousefi,2015

2. Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data

3. Deep Recurrent Neural Networks for Hyperspectral Image Classification

4. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

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