New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Ghamisi, Pedram; Maggiori, Emmanuel; Li, Shutao; Souza, Roberto; Tarablaka, Yuliya; Moser, Gabriele; Giorgi, Andrea; Fang, Leyuan; Chen, Yushi; Chi, Mingmin; B. Serpico, Sebastiano; Atli Benediktsson, Jón

doi:10.1109/mgrs.2018.2854840

Public

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Published in IEEE Geoscience and Remote Sensing Magazine • Sep 1, 2018

Authors:

Pedram Ghamisi

Emmanuel Maggiori

Shutao Li

Abstract

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolution...

Subject

Hyperspectral imaging

Remote sensing

Pattern recognition (psychology)

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