SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images Article Swipe
Xin Li
,
Feng Xu
,
Yong Xi
,
Deqing Chen
,
Runliang Xia
,
Baoliu Ye
,
Hongmin Gao
,
Ziqi Chen
,
Xin Lyu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15235610
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15235610
Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15235610
- https://www.mdpi.com/2072-4292/15/23/5610/pdf?version=1701588407
- OA Status
- gold
- Cited By
- 38
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389286635
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- OpenAlex ID
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https://openalex.org/W4389286635Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs15235610Digital Object Identifier
- Title
-
SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-03Full publication date if available
- Authors
-
Xin Li, Feng Xu, Yong Xi, Deqing Chen, Runliang Xia, Baoliu Ye, Hongmin Gao, Ziqi Chen, Xin LyuList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15235610Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/15/23/5610/pdf?version=1701588407Direct 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/2072-4292/15/23/5610/pdf?version=1701588407Direct OA link when available
- Concepts
-
Computer science, Segmentation, Discriminative model, Spectral space, Artificial intelligence, Feature (linguistics), Remote sensing, Context (archaeology), Pattern recognition (psychology), Geography, Mathematics, Pure mathematics, Archaeology, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
38Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 22Per-year citation counts (last 5 years)
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63Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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