A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification

TL;DR

This paper introduces a novel CNN architecture with mapping layers that effectively reduce redundancy in hyperspectral images, leading to improved classification accuracy and reduced training time.

Contribution

The paper proposes a new MCNN model with mapping layers that maintain energy, reduce layers, and improve spectral-spatial feature extraction for hyperspectral image classification.

Findings

Achieved over 98% accuracy on three hyperspectral datasets.

Reduced training time compared to traditional CNNs.

Effectively mitigated accuracy decline with deeper networks.

Abstract

In this paper, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancy and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on convolutional layer to extract the spectral-spatial features for HSI. We tested our MCNN on three datasets of Indian Pines, University of Pavia and Salinas, and we achieved the classification accuracy of 98.3%, 99.5% and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve the classification accuracy and save much time consumption.