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Effect Analysis of Spectral and Spatial Variations on Attention-Based Cropland Extraction Networks.

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Title: Effect Analysis of Spectral and Spatial Variations on Attention-Based Cropland Extraction Networks.
Authors: Cheng, Lin1 (AUTHOR), Deng, Cailong2,3 (AUTHOR), Zhou, Chaohu2,3 (AUTHOR), Zhang, Yong4 (AUTHOR), Lu, Haojian4,5 (AUTHOR), Li, Zhen5,6 (AUTHOR), Chen, Shiyu1,2,6 (AUTHOR) csy_hy@xynu.edu.cn
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1501. 25p.
Subjects: Spatial resolution, Near infrared radiation, Remote sensing, Image segmentation, Spectral sensitivity, Deep learning, Agricultural mapping
Abstract: Highlights: What are the main findings? Spectral and spatial resolutions exhibit clear linear relationships with cropland segmentation accuracy in attention-based models. A spectral–spatial coupling model based on Iso-IoU effectively quantifies the trade-off between band number and spatial resolution. What are the implications of the main findings? Spectral information can partially compensate for spatial resolution loss, especially for models with stronger spectral utilization capability. The proposed framework provides practical guidance for optimizing input configurations and model selection in agricultural remote sensing applications. Accurate extraction of cropland is essential for optimizing regional land-use structure and ensuring food security. Although attention-based deep learning has advanced cropland extraction, the lack of a quantitative framework to evaluate the trade-off between spectral band count and spatial resolution hinders optimal sensor configuration. To address this gap, we employ two representative attention-based segmentation networks, BsiNet and REAUnet, to conduct controlled spectral–spatial variation experiments, and proposes an equivalent IoU (Iso-IoU) equivalent model to quantify their complementary relationship. By conducting experiments with multiple band combinations and multi-scale spatial resolutions, we quantitatively evaluate the respective contributions of spectral and spatial information to model performance and further analyze their coupling relationship. The results show that: (1) model performance is positively correlated with spectral richness (i.e., band count), where four-band configurations achieve an IoU improvement of approximately 1.5–4% compared with single-band inputs. While the inclusion of the near-infrared (NIR) band consistently yields the highest accuracy within each band count group, the total number of available spectral bands remains the primary driver of segmentation performance; (2) model performance is more sensitive to spatial resolution, and the IoU decreases by about 5–7% on average when the spatial resolution is degraded to one-quarter of the original resolution; (3) a quantifiable complementary relationship exists between spectral band combinations and spatial resolution, which can be described by the proposed Iso-IoU model; (4) the two attention-based networks examined in this study exhibit stable error tendencies in cropland extraction, with consistent false-positive and false-negative patterns. These findings provide practical guidance for cropland extraction with remote sensing images. Prioritizing NIR information and maintaining sufficient spatial resolution are critical for preserving segmentation accuracy, while the Iso-IoU model enables quantitative optimization of spectral–spatial configurations under sensor constraints. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Abstract
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  Data: Highlights: What are the main findings? Spectral and spatial resolutions exhibit clear linear relationships with cropland segmentation accuracy in attention-based models. A spectral–spatial coupling model based on Iso-IoU effectively quantifies the trade-off between band number and spatial resolution. What are the implications of the main findings? Spectral information can partially compensate for spatial resolution loss, especially for models with stronger spectral utilization capability. The proposed framework provides practical guidance for optimizing input configurations and model selection in agricultural remote sensing applications. Accurate extraction of cropland is essential for optimizing regional land-use structure and ensuring food security. Although attention-based deep learning has advanced cropland extraction, the lack of a quantitative framework to evaluate the trade-off between spectral band count and spatial resolution hinders optimal sensor configuration. To address this gap, we employ two representative attention-based segmentation networks, BsiNet and REAUnet, to conduct controlled spectral–spatial variation experiments, and proposes an equivalent IoU (Iso-IoU) equivalent model to quantify their complementary relationship. By conducting experiments with multiple band combinations and multi-scale spatial resolutions, we quantitatively evaluate the respective contributions of spectral and spatial information to model performance and further analyze their coupling relationship. The results show that: (1) model performance is positively correlated with spectral richness (i.e., band count), where four-band configurations achieve an IoU improvement of approximately 1.5–4% compared with single-band inputs. While the inclusion of the near-infrared (NIR) band consistently yields the highest accuracy within each band count group, the total number of available spectral bands remains the primary driver of segmentation performance; (2) model performance is more sensitive to spatial resolution, and the IoU decreases by about 5–7% on average when the spatial resolution is degraded to one-quarter of the original resolution; (3) a quantifiable complementary relationship exists between spectral band combinations and spatial resolution, which can be described by the proposed Iso-IoU model; (4) the two attention-based networks examined in this study exhibit stable error tendencies in cropland extraction, with consistent false-positive and false-negative patterns. These findings provide practical guidance for cropland extraction with remote sensing images. Prioritizing NIR information and maintaining sufficient spatial resolution are critical for preserving segmentation accuracy, while the Iso-IoU model enables quantitative optimization of spectral–spatial configurations under sensor constraints. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18101501
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        Text: English
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        PageCount: 25
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    Subjects:
      – SubjectFull: Spatial resolution
        Type: general
      – SubjectFull: Near infrared radiation
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Image segmentation
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      – SubjectFull: Spectral sensitivity
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      – SubjectFull: Deep learning
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      – SubjectFull: Agricultural mapping
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    Titles:
      – TitleFull: Effect Analysis of Spectral and Spatial Variations on Attention-Based Cropland Extraction Networks.
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              Text: May2026
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              Y: 2026
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