Automatic Change Detection of Planetary Striped Landform on Mars Surface Article Swipe
Fengqi Zhang
,
Weifeng Hao
,
Mao Ye
,
Zhigang Tu
,
Fei Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3371015
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3371015
Cameras carried by satellites take large-scale images of the planetary surface, which provides data support for exploration missions. With the progress of deep space exploration, the demands to identify surface changes are gradually increasing. There are few studies that focus on planetary surface automatic change detection since it faces the challenges of difficult registration, strong pseudochange, poor automatic detection capacity, etc. To handle these challenges, we propose a deep learning lightweight model SLCD-Net (i.e., striped landform change detection model) for patchwise change detection of the planetary striped landform. SLCD-Net uses the dual-input-based siamese network as the overall architecture to learn the deep semantic information of pre- and posttemporal images. Importantly, SLCD-Net designs a dual-feature multilevel complementary fusion module between two branches of the siamese network, which can learn the cross-temporal complementary features and eliminate the noise impact. Besides, SLCD-Net exploits a spatial attention module after the fusion unit, which further helps to weaken the noise that is produced by fusion. Furthermore, we construct a dataset about dark slope streak (DSS, a striped landform type). Training on our DSS dataset enables SLCD-Net to have an advantage in identifying the variation of DSS. The SLCD-Net's detection performance on multiple planetary surface datasets including our DSS test set proves that it has achieved a breakthrough in robustness and generalization. Not limited to the patchwise task, the change map generated by SLCD-Net applied to different regions also proves that it can be effectively used for pixelwise Mars striped landform change detection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2024.3371015
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/10452792.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392251694
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392251694Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2024.3371015Digital Object Identifier
- Title
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Automatic Change Detection of Planetary Striped Landform on Mars SurfaceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Fengqi Zhang, Weifeng Hao, Mao Ye, Zhigang Tu, Fei LiList of authors in order
- Landing page
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https://doi.org/10.1109/jstars.2024.3371015Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/10452792.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/10452792.pdfDirect OA link when available
- Concepts
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Mars Exploration Program, Astrobiology, Landform, Planetary surface, Remote sensing, Geology, Surface (topology), Geomorphology, Physics, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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