Cited by 260Remote-Sensing Image ClassificationRemote Sensing and Land UseAdvanced Neural Network Applications

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A patch-based convolutional neural network for remote sensing image classification

Sharma, Atharva, Liu, Xiuwen, Yang, Xiaojun, Shi, Di·2017·Neural Networks

DOI 10.1016/j.neunet.2017.07.017

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Abstract

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas.

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Authors: Sharma, A., Liu, X., Yang, X., & Shi, D.
Affiliations: Florida State University (US)
University of Kansas (US)
Corresponding author: Sharma, Atharva
ORCID: Sharma, Atharva
Liu, Xiuwen
Yang, Xiaojun
Venue: Neural Networks
Publisher: Elsevier BV
Published: 2017-11
Volume: 95
Pages: 19-28
ISSN: 0893-6080 (ISSN-L)
DOI: 10.1016/j.neunet.2017.07.017
OpenAlex: W2743142445
Cited by: 260
References: 63
Citations by year: 2026: 15 · 2025: 21 · 2024: 27 · 2023: 42 · 2022: 35 · 2021: 47 · 2020: 36 · 2019: 24 · 2018: 13
FWCI: 16.55
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neural Networks (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: land cover classification, remote sensing image classification, patch-based convolutional neural network, medium-resolution satellite imagery, top-of-atmosphere reflectance, Everglades
MeSH terms: Image Processing, Computer-Assisted, Neural Networks, Computer, Remote Sensing Technology

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A patch-based convolutional neural network for remote sensing image classification

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