Uchaev, D.; Uchaev, D. Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering. Sensors2023, 23, 2499.
Uchaev, D.; Uchaev, D. Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering. Sensors 2023, 23, 2499.
Uchaev, D.; Uchaev, D. Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering. Sensors2023, 23, 2499.
Uchaev, D.; Uchaev, D. Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering. Sensors 2023, 23, 2499.
Abstract
In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification. However, the proposed network models have a higher model complexity and do not provide high classification accuracy if few-shot learning is used. This paper pre-sents an HSI classification method that combines random patches network (RPNet) and re-cursive filtering (RF) to obtain informative deep features. The proposed method first convolves image bands with random patches to extract multi-level deep RPNet features. Thereafter, the RPNet feature set is subjected to dimension reduction through principal component analysis (PCA) and the extracted components are filtered using the RF procedure. Finally, HSI spectral features and the obtained RPNet-RF features are combined to classify the HSI using a support vector machine (SVM) classifier. In order to test the performance of the proposed RPNet-RF method, some experiments were performed on three widely known datasets using a few training samples for each class and classification results were compared with those obtained by other advanced HSI classification methods adopted for small training samples. The comparison showed that the RPNet-RF classification is characterized by higher values of such evaluation metrics as overall accuracy and Kappa coefficient (https://github.com/UchaevD/RPNet-RF).
Keywords
hyperspectral data; few-shot learning; deep features; convolution kernels; edge-preserving filtering
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
