Regularized feature extractions for hyperspectral data classification. Published in: IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing ...
The main purpose of feature extraction is trying to mitigate the Hughes phenomena when the training sample size is small. For using DAFE and NDA, ...
In this paper, a new sequential feature extraction and classification algorithm is proposed for improving the classification accuracy of reject region data. 1 ...
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What is feature extraction in hyperspectral data?
What is hyperspectral data classification?
The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and ...
The classification accuracies of RBF-based SVM using two feature extractions with three regularization techniques are evaluated. The results of two ...
In this Special Issue, we aim to compile state-of-the-art research on how to tackle the “big data” problem of extracting the most useful information out of the ...
Jan 20, 2024 · In this paper, we analyze regularized non-linear methods in the context of hyperspectral image classification. For this purpose, we compare ...
Apr 25, 2021 · Abstract—Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC).
Jun 10, 2021 · The proposed method helps to prevent CNN from becoming over-confident. We empirically show that, in improving generalization performance, ...
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