A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification

08/26/2019
by   Rui Li, et al.
4

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 results demonstrate that the proposed MCNN can significantly improve the classification accuracy and save much time consumption.

READ FULL TEXT

page 1

page 6

page 7

page 10

page 11

research
02/07/2020

Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network

The Hyperspectral image (HSI) classification is a standard remote sensin...
research
11/19/2021

A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification

In the proposed SEHybridSN model, a dense block was used to reuse shallo...
research
03/28/2022

MixNN: A design for protecting deep learning models

In this paper, we propose a novel design, called MixNN, for protecting d...
research
01/20/2020

Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification

Convolutional neural networks (CNN) have made significant advances in hy...
research
02/04/2020

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

In this paper, we propose an efficient and effective framework to fuse h...
research
12/01/2021

Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification

Recently, convolutional neural network (CNN) techniques have gained popu...
research
11/18/2020

Convolutional Autoencoder for Blind Hyperspectral Image Unmixing

In the remote sensing context spectral unmixing is a technique to decomp...

Please sign up or login with your details

Forgot password? Click here to reset