Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification.

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ID: 104845
2020
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Abstract
Nowadays, deep learning methods, especially the convolutional neural networks (CNNs), have shown impressive performance on extracting abstract and high-level features from the hyperspectral image. However, the general training process of CNNs mainly considers the pixelwise information or the samples' correlation to formulate the penalization while ignores the statistical properties especially the spectral variability of each class in the hyperspectral image. These sample-based penalizations would lead to the uncertainty of the training process due to the imbalanced and limited number of training samples. To overcome this problem, this article characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning. Based on the Fisher discrimination criterion, the loss penalizes the sample variance of each class distribution to decrease the intraclass variance of the training samples. Moreover, an additional diversity-promoting condition is added to enlarge the interclass variance between different class distributions, and this could better discriminate samples from different classes in the hyperspectral image. Finally, the statistical estimation form of the statistical loss is developed with the training samples through multivariant statistical analysis. Experiments over the real-world hyperspectral images show the effectiveness of the developed statistical loss for deep learning.
Reference Key
gong2020statisticalieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gong, Zhiqiang;Zhong, Ping;Hu, Weidong;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2020
DOI
10.1109/TNNLS.2020.2978577
URL
Keywords
Machine learning noise filtering class noise crash injury severity data sampling

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