Analysis and evaluation of learning classifier systems applied to hyperspectral image classification

2005 ◽  
Author(s):  
A. Quirin ◽  
J. Korczak ◽  
M.V. Butz ◽  
D.E. Goldberg
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Hyperspectral Image Classification ◽  
Analysis And Evaluation ◽  
Learning Classifier ◽  
Evaluation Of Learning

scholarly journals Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning

2020 ◽  
Vol 12 (23) ◽  
pp. 3879
Author(s):  
Guangxing Wang ◽  
Peng Ren
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
The Real ◽  
Learning Classifier ◽  
Feature Variability ◽  
High Level

Deep learning classifiers exhibit remarkable performance for hyperspectral image classification given sufficient labeled samples but show deficiency in the situation of learning with limited labeled samples. Active learning endows deep learning classifiers with the ability to alleviate this deficiency. However, existing active deep learning methods tend to underestimate the feature variability of hyperspectral images when querying informative unlabeled samples subject to certain acquisition heuristics. A major reason for this bias is that the acquisition heuristics are normally derived based on the output of a deep learning classifier, in which representational power is bounded by the number of labeled training samples at hand. To address this limitation, we developed a feature-oriented adversarial active learning (FAAL) strategy, which exploits the high-level features from one intermediate layer of a deep learning classifier for establishing an acquisition heuristic based on a generative adversarial network (GAN). Specifically, we developed a feature generator for generating fake high-level features and a feature discriminator for discriminating between the real high-level features and the fake ones. Trained with both the real and the fake high-level features, the feature discriminator comprehensively captures the feature variability of hyperspectral images and yields a powerful and generalized discriminative capability. We leverage the well-trained feature discriminator as the acquisition heuristic to measure the informativeness of unlabeled samples. Experimental results validate the effectiveness of both (i) the full FAAL framework and (ii) the adversarially learned acquisition heuristic, for the task of classifying hyperspectral images with limited labeled samples.

scholarly journals Deep Manifold Embedding for Hyperspectral Image Classification

2021 ◽  
pp. 1-14
Author(s):  
Zhiqiang Gong ◽  
Weidong Hu ◽  
Xiaoyong Du ◽  
Ping Zhong ◽  
Panhe Hu
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Manifold Embedding
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