spectralnet's Introduction

SpectralNET a 2D wavelet CNN for Hyperspectral Image Classification.

Authors: Tanmay CHAKRABORTY & Utkarsh TREHAN

Description

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs, FuSENets. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatialspectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods.

Link to paper

http://arxiv.org/abs/2104.00341

Model

Fig: Proposed SpectralNet (Wavelet CNN) Model for hyperspectral image (HSI) classification.

Prerequisites

Results

Salinas Scene (SS) dataset

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

Cite the paper if you are using this work

@article{chakraborty2021spectralnet,

title={SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification},

author={Chakraborty, Tanmay and Trehan, Utkarsh},

journal={arXiv preprint arXiv:2104.00341},

year={2021}

}

Acknowledgement

https://github.com/gokriznastic/HybridSN
https://github.com/menon92/WaveletCNN

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spectralnet's Issues

Train-test-set split

Hello Tanmay, hello Utkarsh,

your work is an interesting contribution to the small research field of hyperspectral imaging, and I really like your approach.

I am currently working on a similar technique and would like to evaluate my method on the Hyperspectral Remote Sensing Scenes dataset as well.
While reviewing your code, I came across your patch generation and train-val test split, which seems kind of strange to me.

You generate patches with a stride of 1, which results in a lot of overlap between patches. By randomly splitting these patches into train and test set,
the training set contains patches that are very similar to the patches in the test set.
Therefore, you cannot use these experiments to prove the generalization properties of your method.

Could you please clarify if I missed something here?
Otherwise, the claim of "state of the art" is misleading.

Yours sincerely,
Leon Varga

tanmay-ty / spectralnet Goto Github PK