Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
This work addresses hyperspectral image classification for remote sensing applications, but it is incremental as it builds on existing methods with a hybrid approach.
The paper tackles the challenges of high dimensionality and limited labeled data in hyperspectral image classification by proposing a deep learning architecture using 3D convolutional neural networks with spectral partitioning, achieving competitive performance on Indian Pines and Salinas datasets compared to current methods.
Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land cover classifications. However, HSIs processing are tangled with the problem of high dimensionality and limited amount of labelled data. To address these challenges, this paper proposes a deep learning architecture using three dimensional convolutional neural networks with spectral partitioning to perform effective feature extraction. We conduct experiments using Indian Pines and Salinas scenes acquired by NASA Airborne Visible/Infra-Red Imaging Spectrometer. In comparison to prior results, our architecture shows competitive performance for classification results over current methods.