Hybrid-Spectral-CNN-for-HSI

Description

The HybridSN is spectral-spatial 3D-CNN followed by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN further learns more abstract level spatial representation.

Model

Fig: Proposed HybridSpectralNet (HybridSN) Model with 3D and 2D convolutions for hyperspectral image (HSI) classification.

Prerequisites

• Anaconda 2.7
• Tensorflow 1.3
• Keras 2.0

Results

Indian Pines (IP) dataset

Fig.2 The IN dataset classification result (Overall Accuracy 99.81%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map. (d) Class legend.

University of Pavia (UP) dataset

Fig.3 The UP dataset classification result (Overall Accuracy 99.99%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map. (d) Class legend.

Salinas Scene (SS) dataset

Fig.4 The UP dataset classification result (Overall Accuracy 100%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map.