Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery Article Swipe
Shiran Song
,
Jianhua Liu
,
Yuan Liu
,
Guoqiang Feng
,
Hui Han
,
Yuan Yao
,
Mingyi Du
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20020397
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20020397
High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.
Related Topics
Concepts
Computer science
Artificial intelligence
Convolutional neural network
Deep learning
Context (archaeology)
Focus (optics)
Pattern recognition (psychology)
Cognitive neuroscience of visual object recognition
Object detection
Feature extraction
Object (grammar)
Representation (politics)
Field (mathematics)
Computer vision
Remote sensing
Geography
Physics
Archaeology
Optics
Mathematics
Politics
Pure mathematics
Law
Political science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20020397
- https://www.mdpi.com/1424-8220/20/2/397/pdf?version=1578658366
- OA Status
- gold
- Cited By
- 49
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3000376294
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3000376294Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20020397Digital Object Identifier
- Title
-
Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing ImageryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-10Full publication date if available
- Authors
-
Shiran Song, Jianhua Liu, Yuan Liu, Guoqiang Feng, Hui Han, Yuan Yao, Mingyi DuList of authors in order
- Landing page
-
https://doi.org/10.3390/s20020397Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/2/397/pdf?version=1578658366Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/2/397/pdf?version=1578658366Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Convolutional neural network, Deep learning, Context (archaeology), Focus (optics), Pattern recognition (psychology), Cognitive neuroscience of visual object recognition, Object detection, Feature extraction, Object (grammar), Representation (politics), Field (mathematics), Computer vision, Remote sensing, Geography, Physics, Archaeology, Optics, Mathematics, Politics, Pure mathematics, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
49Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 7, 2023: 16, 2022: 9, 2021: 12Per-year citation counts (last 5 years)
- References (count)
-
64Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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