Extraction of cropland field parcels with high resolution remote sensing using multi-task learning

Xu, Leilei; Yang, Peng; Yu, Juanjuan; Peng, Fei; Xu, Jun; Song, Shuling; Wu, Yue

doi:10.1080/22797254.2023.2181874

Cited by 11 publications

(4 citation statements)

References 62 publications

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“…In the study [29], Xu et al introduced a multi-task cascade network model called SGENet for the extraction of farmland plot information. This model was designed to automatically learn multi-scale and multi-level features, enabling it to handle complex planting scenarios and different scales of farmland plots.…”

Section: Related Workmentioning

confidence: 99%

GF-UNet:A Cropland Extraction Method Based on Attention Gate and Adaptive Feature Fusion

Li,

Wang

2024

IJCAI

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Cropland plays a critical role in maintaining national food security, but its extraction is often hindered by factors such as type of cropland, crop category, and surrounding vegetation, resulting in low extraction accuracy. This paper proposes a cropland extraction network, called GF-UNet, to address the challenges of accurately extracting cropland from very high-resolution (VHR) remote sensing images. GF-UNet builds on the Attention U-Net network and introduces Attention Gates (AGs) to improve the ability to discriminate between similar features of cropland and non-cropland in complex situations. This helps to improve the accuracy of cropland extraction. In addition, an Adaptive Feature Fusion Module (AFFM) is incorporated to integrate multi-scale cropland features, further enhancing the network's ability to identify cropland. The Spatial Feature Extraction Module (SFEM) is also introduced into the skip connection to improve the extraction of detailed features in the results. The research data used in the study consists of GF-2 satellite images of Xuan 'en County, Hubei Province, from June to September 2019. Comparative experiments were conducted with SOTA models, the results demonstrate that GF-UNet outperforms the other models in terms of accuracy, F1-score, and IoU. The accuracy, F1-score, and IoU of GF-UNet were reported as 91.25%, 92.41%, and 84.56%, respectively. The study also explores the impact of SFEM and AFFM on the experimental results. Compared to existing SOTA methods, GF-UNet proves to be more suitable for cropland extraction in complex scenes, providing a practical approach to addressing the challenges of cropland extraction in such scenarios. Povzetek: Članek predstavlja omrežje GF-UNet za natančno izločanje kmetijskih površin iz slik visoke ločljivosti s pomočjo daljinskega zaznavanja. GF-UNet uporablja modul za adaptivno združevanje značilnosti in prostorski modul za izboljšanje delovanja.

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Section: Related Workmentioning

confidence: 99%

GF-UNet:A Cropland Extraction Method Based on Attention Gate and Adaptive Feature Fusion

Li,

Wang

2024

IJCAI

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“…To ensure reliable and comprehensive evaluation of boundary delineation algorithms, it has become a common practice to adopt established metrics that have gained popularity in this domain [13,18]. These metrics encompass a combination of pixel-based error measurements, including precision, recall, F1 score, intersection over union (IoU), and overall accuracy (OA), which offer detailed insights into the algorithm's performance at the pixel level.…”

Section: Accuracy Assessment and Error Metricsmentioning

confidence: 99%

“…Moreover, Xu et al proposed a unique combination of semantic and edge detection within a cascaded multitask framework. Their architecture fused a refinement network with fixed-edge local connectivity to ensure robust boundary delineation on high-resolution images, reporting an F1 score of 0.86 [18]. In another work, Long et al introduced the lightweight multitask network BsiNet, employing a singular encoder-decoder mechanism.…”

Section: Introductionmentioning

confidence: 99%

FAUNet: Frequency Attention U-Net for Parcel Boundary Delineation in Satellite Images

Awad,

Erer

2023

Remote Sensing

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Parcel detection and boundary delineation play an important role in numerous remote sensing applications, such as yield estimation, crop type classification, and farmland management systems. Consequently, achieving accurate boundary delineation remains a prominent research area within remote sensing literature. In this study, we propose a straightforward yet highly effective method for boundary delineation that leverages frequency attention to enhance the precision of boundary detection. Our approach, named Frequency Attention U-Net (FAUNet), builds upon the foundational and successful U-Net architecture by incorporating a frequency-based attention gate to enhance edge detection performance. Unlike many similar boundary delineation methods that employ three segmentation masks, our network employs only two, resulting in a more streamlined post-processing workflow. The essence of frequency attention lies in the integration of a frequency gate utilizing a high-pass filter. This high-pass filter output accentuates the critical high-frequency components within feature maps, thereby significantly improves edge detection performance. Comparative evaluation of FAUNet against alternative models demonstrates its superiority across various pixel-based and object-based metrics. Notably, FAUNet achieves a pixel-based precision, F1 score, and IoU of 0.9047, 0.8692, and 0.7739, respectively. In terms of object-based metrics, FAUNet demonstrates minimal over-segmentation (OS) and under-segmentation (US) errors, with values of 0.0341 and 0.1390, respectively.

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“…In the context of urban building classification, the utilization of a multi-task learning modeling approach with five interdependent building labels consistently demonstrates superior accuracy and efficiency compared to both single-task learning and classical hard parameter sharing methods [39]. In agrarian contexts, prior studies utilizing multi-task deep convolutional neural networks have showcased marked advancements in delineating agricultural perimeters, field expanses, and cropping patterns [40,41]. Contrastingly, in the realm of terrace research, the potential of multi-task learning remains largely untapped.…”

Section: Introductionmentioning

confidence: 99%

Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning

Zhang,

Huang

et al. 2024

Remote Sensing

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Terrace detection and ridge extraction from high-resolution remote sensing imagery are crucial for soil conservation and grain production on sloping land. Traditional methods use low-to-medium resolution images, missing detailed features and lacking automation. Terrace detection and ridge extraction are closely linked, with each influencing the other’s outcomes. However, most studies address these tasks separately, overlooking their interdependence. This research introduces a cutting-edge, multi-scale, and multi-task deep learning framework, termed DTRE-Net, designed for comprehensive terrace information extraction. This framework bridges the gap between terrace detection and ridge extraction, executing them concurrently. The network incorporates residual networks, multi-scale fusion modules, and multi-scale residual correction modules to enhance the model’s robustness in feature extraction. Comprehensive evaluations against other deep learning-based semantic segmentation methods using GF-2 terraced imagery from two distinct areas were undertaken. The results revealed intersection over union (IoU) values of 85.18% and 86.09% for different terrace morphologies and 59.79% and 73.65% for ridges. Simultaneously, we have confirmed that the connectivity of results is improved when employing multi-task learning for ridge extraction compared to directly extracting ridges. These outcomes underscore DTRE-Net’s superior capability in the automation of terrace and ridge extraction relative to alternative techniques.

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Extraction of cropland field parcels with high resolution remote sensing using multi-task learning

Cited by 11 publications

References 62 publications

GF-UNet:A Cropland Extraction Method Based on Attention Gate and Adaptive Feature Fusion

GF-UNet:A Cropland Extraction Method Based on Attention Gate and Adaptive Feature Fusion

FAUNet: Frequency Attention U-Net for Parcel Boundary Delineation in Satellite Images

Dual-Task Network for Terrace and Ridge Extraction: Automatic Terrace Extraction via Multi-Task Learning

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