Semi-supervised semantic segmentation framework with pseudo supervisions for land-use/land-cover mapping in coastal areas

Chen, Jifa; Sun, Buyang; Wang, Lizhe; Fang, B.; Chang, Yuan; Li, Yukun; Zhang, Junpeng; Lyu, Xianlin; Chen, Gang

doi:10.1016/j.jag.2022.102881

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…Li et al [166] combined self-training and consistency regularization for cross-domain SSRSI, which learned the transfer invariance and rotation consistency under image perturbation and generated pseudo labels for unlabeled data. Chen et al [167] leveraged the feature perturbation to conduct regularization constraints, and provided the one-hot pseudo supervisions for further self-training. SemiRoadExNet [168] proposed a GAN-based semi-supervised road extraction network, which leveraged the potential information of low-confidence pixels in pseudo labels by entropy maps generated by the segmentation network.…”

Section: B Semi-supervised and Weakly-supervised Ssrsimentioning

confidence: 99%

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

Huang,

Jiang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semantic segmentation of remote sensing images (SSRSI), which aims to assign a category to each pixel in remote sensing images, plays a vital role in a broad range of applications, such as environmental monitoring, urban planning, and land resource utilization. Recently, with the successful application of deep learning in remote sensing, a substantial amount of work has been aimed at developing SSRSI methods using deep learning models. In this survey, we provide a comprehensive review of SSRSI. Firstly, we review the current mainstream semantic segmentation models based on deep learning. Next, we analyze the main challenges faced by SSRSI and comprehensively summarize the current research status of deep learning-based SSRSI, especially some new directions in SSRSI are outlined, including semi-supervised and weakly-supervised SSRSI, unsupervised domain adaption (UDA) in SSRSI, multi-modal data fusion-based SSRSI, and pretrained models for SSRSI. Then, we examine the most widely used datasets and metrics and review the quantitative results and experimental performance of some representative methods of SSRSI. At last, we discuss promising future research directions in this area.

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Section: B Semi-supervised and Weakly-supervised Ssrsimentioning

confidence: 99%

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

Huang,

Jiang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Water quality prediction, leveraging partial labels. [179] Statistical modeling and environmental data…”

Section: Semisupervised Learning and Environmental Data Semisupervisedmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Drogkoula,

Kokkinos,

Samaras

2023

Applied Sciences

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This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

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“…CPS [15] also used two parallel segmentation networks, making the cross pseudo supervision that enforces the consistency between the two segmentation networks. Some works [40], [43], [44] also combining the consistency training with pseudo label to exploit the unlabeled data. The consistency constraint between the predictions of augmented images will make the decision function lie in the low-density region, promoting the model's recognition ability.…”

Section: B Semi-supervised Semantic Segmentationmentioning

confidence: 99%

Semisupervised Semantic Segmentation With Certainty-Aware Consistency Training for Remote Sensing Imagery

Guo

Wang

Xiang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semi-supervised learning is a forcible method to lessen the cost of annotation for remote sensing semantic segmentation tasks. Recent related researches indicate that consistency training is one of the most effective strategies in semisupervised learning. The core of consistency training is maintaining model outputs consistent under various perturbations. However, the current consistency training-based semi-supervised semantic segmentation frameworks lack the analysis of model uncertainty, which increases the generation of semantic ambiguity on remote sensing images. Therefore, we propose the certainty-aware consistency training (CACT) strategy to mitigate the influence of semantic ambiguity caused by model uncertainty. The certainty-aware consistency training strategy consists of two novel parts: certainty-aware consistency correction (CACC) and class-balanced adaptive threshold (CBAT). The CACC starts with generating a high-quality prediction target, then models the importance of the consistent output target and corrects the output predictions according to the certainty map, increasing the focus on reliable predictions. The CBAT uses a dynamic class-balanced adaptive threshold to filter out unreliable predictions, further reducing the impact of semantic ambiguity. Finally, considerable experimental results on the DLRSD, WHDLD, and Potsdam demonstrate that our framework has excellent performance on semi-supervised remote sensing semantic segmentation scenarios.

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Semi-supervised semantic segmentation framework with pseudo supervisions for land-use/land-cover mapping in coastal areas

Cited by 15 publications

References 36 publications

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Semisupervised Semantic Segmentation With Certainty-Aware Consistency Training for Remote Sensing Imagery

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