Lake water body extraction of optical remote sensing images based on semantic segmentation

Zhong, Hai-Feng; Sun, Hong-Mei; Han, Dong-Nuo; Li, Zenghu; Jia, Rui-Sheng

doi:10.1007/s10489-022-03345-2

Cited by 19 publications

(6 citation statements)

References 40 publications

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“…For the large-scale visual elements in the image, the receptive field can only cover its local area, which can easily cause wrong recognition results, and for the small-scale visual elements in the image. The challenge of exploiting multi-scale segmentation is to automatically select the best consecutive segmentation scale analysis Zhong et al, 2022a). Most methods are based on hierarchical structure or parallel structure, combined with an attention mechanism to achieve multi-scale feature fusion.…”

Section: Multi-scale Strategy-based Methodsmentioning

confidence: 99%

Deep learning-based semantic segmentation of remote sensing images: a review

Shen

et al. 2023

Front. Ecol. Evol.

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Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

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Section: Multi-scale Strategy-based Methodsmentioning

confidence: 99%

Deep learning-based semantic segmentation of remote sensing images: a review

Shen

et al. 2023

Front. Ecol. Evol.

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“…In the field of water extraction, numerous scholars have optimized and enhanced existing models to improve the accuracy of segmentation for water extraction. For instance, Zhong et al [15] incorporated depthwise separable convolutions in the encoder to enhance the capacity for extracting global information from remote sensing images when identifying lake water features. Li et al [16] devised a dense network for precise water extraction and integrated a bidirectional channel attention mechanism to mitigate noise interference during lake analysis.…”

Section: Introductionmentioning

confidence: 99%

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

Li,

Weng,

Zheng

et al. 2024

Applied Sciences

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The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%.

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“…Semantic segmentation of remote sensing images aims to produce pixel-wise categorical labels to facilitate interpretation of the remote sensing data [1], [2], [3]. The semantically parsed annotation enables an intuitive perception of targets, therefore, has been widely adopted in downstream tasks, such as land-cover mapping [4], [5], water resources management [6], [7] and disaster assessment [8], [9], among others.…”

Section: Introductionmentioning

confidence: 99%

A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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In remotely sensed images, high intra-class variance and inter-class similarity are ubiquitous due to complex scenes and objects with multivariate features, making semantic segmentation a challenging task. Deep convolutional neural networks can solve this problem by modelling the context of features and improving their discriminability. However, current learning paradigms model the feature affinity in spatial dimension and channel dimension separately and then fuse them in a sequential or parallel manner, leading to suboptimal performance. In this study, we first analyze this problem practically and summarize it as attention bias that reduces the capability of network in distinguishing weak and discretely distributed objects from widerange objects with internal connectivity, when modeled only in spatial or channel domain. To jointly model both spatial and channel affinity, we design a synergistic attention module (SAM), which allows for channel-wise affinity extraction while preserving spatial details. In addition, we propose a synergistic attention perception neural network (SAPNet) for the semantic segmentation of remote sensing images. The hierarchicalembedded synergistic attention perception module aggregates SAM-refined features and decoded features. As a result, SAPNet enriches inference clues with desired spatial and channel details. Experiments on three benchmark datasets show that SAPNet is competitive in accuracy and adaptability compared with stateof-the-art methods. The experiments also validate the hypothesis of attention bias and the efficiency of SAM.

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Lake water body extraction of optical remote sensing images based on semantic segmentation

Cited by 19 publications

References 40 publications

Deep learning-based semantic segmentation of remote sensing images: a review

Deep learning-based semantic segmentation of remote sensing images: a review

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images

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