DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation

Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on co...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (4), p.818
Main Authors: Zhu, Hongming, Tan, Rui, Han, Letong, Fan, Hongfei, Wang, Zeju, Du, Bowen, Liu, Sicong, Liu, Qin
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer vision
Deep learning
deep neural network
Feature extraction
Image processing
Image segmentation
Learning algorithms
Machine learning
Methods
Modules
multi-spectral images
Neural networks
Remote sensing
Semantics
Spectra
spectrum separable
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Summary:Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimental results obtained on Zurich and Potsdam datasets prove that the spectrum-separable module (SSM) extracts more informative spectral features, and the proposed approach improves the segmentation accuracy without increasing GPU consumption.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14040818