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A patch-based convolutional neural network for remote sensing image classification
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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“Although there are also many freely available medium-resolution (10 m-30 m) satellite images for LULC mapping, including Landsat and Sentinel-2 data, it is difficult to apply conventional DL algorithms directly to these images because of the lack of such fine structures (Sharma et al., 2017).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Sharma et al., 2017
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Metadata
- Authors
- Sharma, A., Liu, X., Yang, X., & Shi, D.
- Affiliations
- Florida State University (US)
- University of Kansas (US)
- Corresponding author
- Sharma, Atharva
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 95
- Pages
- 19-28
- ISSN
- 0893-6080 (ISSN-L)
- 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
- 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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