Inland Water Mapping Based on GA-LinkNet From CyGNSS Data

Yan, Qingyun; Chen, Yuhan; Jin, Shuanggen; Liu, Shuci; Jia, Yan; Zhen, Yinqing; Chen, Tiexi; Huang, Weimin

doi:10.1109/lgrs.2022.3227596

Cited by 20 publications

(12 citation statements)

References 22 publications

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“…Semantic segmentation is a pixel-level classification task, and many semantic segmentation models adopt the encoder-decoder architecture, exemplified by models like Unet [48,49], LinkNet [50,51], PSPNET [52], and more. Various studies utilizing Unet-based approaches have been instrumental in automatically extracting buildings from remote sensing imagery [53,54].…”

Section: Semantic Segmentationmentioning

confidence: 99%

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

Chen,

Yan,

Huang

2023

Remote Sensing

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The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (R2) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.

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Section: Semantic Segmentationmentioning

confidence: 99%

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

Chen,

Yan,

Huang

2023

Remote Sensing

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“…Our ablation studies reveal the critical role of shallow spatial detail information in hyperspectral semantic segmentation tasks, emphasizing the importance of preserving this information in the model's success. The process of feature map aggregation in the decoder is similar to that of LinkNet [41,42], while UperNet is used to combine feature maps from the Encoder. Overall, our approach enables the model to capture both shallow and deep spatial details effectively, leading to an improved performance in hyperspectral semantic segmentation tasks.…”

Section: Decodermentioning

confidence: 99%

“…For the semantic segmentation method, the optimizer to be selected had the default parameters AdamW optimizer [43]. The learning rate is 3 × 10 −4 in [42], 5 × 10 −4 in [11], 1 × 10 −4 in [25], and 6 × 10 −5 in [13,38]. In order to compare various models more fairly, we set the learning rate to 2 × 10 −4 , and set the epoch to 200 to ensure that the model can fully converge.…”

Section: Experimental Platform Parameter Settingsmentioning

confidence: 99%

Shallow-Guided Transformer for Semantic Segmentation of Hyperspectral Remote Sensing Imagery

et al. 2023

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Convolutional neural networks (CNNs) have achieved great progress in the classification of surface objects with hyperspectral data, but due to the limitations of convolutional operations, CNNs cannot effectively interact with contextual information. Transformer succeeds in solving this problem, and thus has been widely used to classify hyperspectral surface objects in recent years. However, the huge computational load of Transformer poses a challenge in hyperspectral semantic segmentation tasks. In addition, the use of single Transformer discards the local correlation, making it ineffective for remote sensing tasks with small datasets. Therefore, we propose a new Transformer layered architecture that combines Transformer with CNN, adopts a feature dimensionality reduction module and a Transformer-style CNN module to extract shallow features and construct texture constraints, and employs the original Transformer Encoder to extract deep features. Furthermore, we also designed a simple Decoder to process shallow spatial detail information and deep semantic features separately. Experimental results based on three publicly available hyperspectral datasets show that our proposed method has significant advantages compared with other traditional CNN, Transformer-type models.

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“…In addition, in terms of water quality monitoring and environmental protection, accurate water body extraction is highly important for assessing the pollution status of water bodies and providing early warning of green tide outbreaks. In the field of remote sensing, the powerful feature learning and generalization capabilities of deep learning provide a new solution for the automatic extraction of water bodies [25,26] and green tides [27,28]. Previous studies have introduced convolutional neural network-based architectures for algae detection.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution

Cui,

Liu,

Chen

et al. 2024

Remote Sensing

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Green tides are marine disasters caused by the explosive proliferation or high concentration of certain large algae in seawater, which causes discoloration of the water body. Accurate monitoring of its distribution area is highly important for early warning and the protection of marine ecology. However, existing deep learning methods have difficulty in effectively identifying green tides with anisotropic characteristics due to the complex and variable shapes of the patches and the wide range of scales. To address this issue, this paper presents an anisotropic green tide patch extraction network (AGE-Net) based on deformable convolution. The main structure of AGE-Net consists of stacked anisotropic feature extraction (AFEB) modules. Each AFEB module contains two branches for extracting green tide patches. The first branch consists of multiple connected dense blocks. The second branch introduces a deformable convolution module and a depth residual module based on a multiresolution feature extraction network for extracting anisotropic features of green tide patches. Finally, an irregular green tide patch feature enhancement module is used to fuse the high-level semantic features extracted from the two branches. To verify the effectiveness of the AGE-Net model, experiments were conducted on the MODIS Green Tide dataset. The results show that AGE-Net has better recognition performance, with F1-scores and IoUs reaching 0.8317 and 71.19% on multi-view test images, outperforming other comparison methods.

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Inland Water Mapping Based on GA-LinkNet From CyGNSS Data

Cited by 20 publications

References 22 publications

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

MFTSC: A Semantically Constrained Method for Urban Building Height Estimation Using Multiple Source Images

Shallow-Guided Transformer for Semantic Segmentation of Hyperspectral Remote Sensing Imagery

Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution

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