Tri-CNN: A three branch model for hyperspectral image classification
Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven
to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have
demonstrated extremely powerful performance in such tasks. However, the lack of training
samples is one of the main contributors to low classification performance. Traditional CNN-
based techniques under-utilize the inter-band correlations of HSI because they primarily use
2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial …
to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have
demonstrated extremely powerful performance in such tasks. However, the lack of training
samples is one of the main contributors to low classification performance. Traditional CNN-
based techniques under-utilize the inter-band correlations of HSI because they primarily use
2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial …
[PDF][PDF] Tri-CNN: A Three Branch Model for Hyperspectral Image Classification. Remote Sens. 2023, 15, 316
Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven
to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have
demonstrated extremely powerful performance in such tasks. However, the lack of training
samples is one of the main contributors to low classification performance. Traditional CNN-
based techniques under-utilize the inter-band correlations of HSI because they primarily use
2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial …
to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have
demonstrated extremely powerful performance in such tasks. However, the lack of training
samples is one of the main contributors to low classification performance. Traditional CNN-
based techniques under-utilize the inter-band correlations of HSI because they primarily use
2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial …
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