gloryofroad / a2s2k-resnet

A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification

Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification

This repository is the official implementation of Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification.

📋 Abstract: Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral-spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral-spatial kernel improved residual network (A²S²K-ResNet) with spectral attention to capture discriminative spectral-spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral-spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A²S²K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated.

Requirements

To install requirements:

conda env create -f environment.yml

To download the dataset and setup the folders, run:

bash setup_script.sh

Training

To train the model(s) in the paper, run this command in the A2S2KResNet folder:

python A2S2KResNet.py -d <IN|UP|KSC> -e 200 -i 3 -p 3 -vs 0.9 -o adam

Results

Our model achieves the following performance on 10% of datasets:

India Pines dataset

Model name	OA
A2S2K-ResNet	98.66 ± 0.004 %

Kennedy Space Center dataset

Model name	OA
A2S2K-ResNet	99.34 ± 0.001 %

University of Pavia dataset

Model name	OA
A2S2K-ResNet	99.85 ± 0.001 %

For deatiled results refer to Table IV-VII of our paper.

Citation

If you use A2S2K-ResNet code in your research, we would appreciate a citation to the original paper:

@article{roy2020attention,
	title={Attention-based adaptive spectral-spatial kernel resnet for hyperspectral image classification},
	author={Swalpa Kumar Roy, and Suvojit Manna, and Tiecheng Song, and Lorenzo Bruzzone},
	journal={IEEE Transactions on Geoscience and Remote Sensing},
	volume={59},
	no.={9},
	pp.={7831-7843},
	year={2021},
	publisher={IEEE}
	}