Two lattice computing approaches for the unsupervised segmentation of hyperspectral images-Reference-Cited by-同舟云学术

Two lattice computing approaches for the unsupervised segmentation of hyperspectral images

Published:2009-06 Issue:10-12 Volume:72 Page:2111-2120
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Graña Manuel,Villaverde Ivan,Maldonado José O.,Hernandez Carmen

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference34 articles.

1. J.W. Boardman, Analysis, understanding and visualization of hyperspectral data as convex sets in n-space, in: Imaging Spectrometry, Proceedings of the SPIE, vol. 2480, 1995, pp. 14–22.

2. Minimum volume transformations for remotely sensed data;Craig;IEEE Trans. Geos. Rem. Sensing,1994

3. Pattern Classification and Scene Analysis;Duda,1973

4. Formal Concept Analysis, Mathematical Foundations;Ganter,1999

5. Digital Image Processing;Gonzalez,2007

Cited by 62 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unsupervised method for estimating the number of endmembers in hyperspectral images;Biomedical Signal Processing and Control;2024-09

2. Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data;Geoscience Frontiers;2023-07

3. Lattice Auto-Associative Memories Induced Multivariate Morphology for Hyperspectral Image Spectral-Spatial Classification;Advances in Intelligent Systems and Computing;2019-05-08

4. Morphological Associative Memories;Wiley Encyclopedia of Electrical and Electronics Engineering;2018-02-22

5. Segmentation for remote sensing image with shape and spectrum prior;Neurocomputing;2016-12