Spectral index-driven FCN model training for water extraction from multispectral imagery

Li, Zhenshi; Zhang, Xueliang; Xiao, Pengfeng

doi:10.1016/j.isprsjprs.2022.08.019

Cited by 18 publications

(15 citation statements)

References 54 publications

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“…Establishing fine-scale urban datasets for landscape/landcover interpretation and deep learning-based research has become a hot research topic in recent years 15 , 17 , 19 . However, present urban landscape datasets typically focused on individual landscapes (e.g., UGS or UBS) 20 , 21 or limited spatial extents (usually covering several cities/provinces) 22 . Some scholars focused on UGS extraction to accurately digitally twin UGS at a fine scale 5 , 21 , 23 , such as Brandt et al .…”

Section: Background and Summarymentioning

confidence: 99%

“… 5 generated 1-meter UGS maps for 31 major cities in China using Google Earth images. Some scholars focused on UBS extraction to address the obstacles posed by the confusion of water with heavy shadows in VHR images 20 , 24 , 25 , like Chen et al . 25 proposed an open water detection method in urban areas using VHR imagery, successfully identifying various types of water bodies.…”

Section: Background and Summarymentioning

confidence: 99%

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Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Xu,

Zhao

2024

Sci Data

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Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human–nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m’s great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.

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Section: Background and Summarymentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Xu,

Zhao

2024

Sci Data

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“…Li et al [9] elucidated a method wherein a spectral indexdriven Fully Convolutional Network (FCN) was devised for the extraction of aquatic regions from multispectral remote sensing images. These multispectral images were first subjected to preprocessing measures, encompassing image enhancement and denoising, with the objective of enhancing the data quality.…”

Section: Figure 1 Schematic Diagram Of Switchgear Intelligent Operati...mentioning

confidence: 99%

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

Hua,

Zhao,

Zhang

et al. 2023

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In addressing the digital and intelligent transformation challenges within the traditional desulfurization process of coal secondary utilization, a perception learning model rooted in semantic segmentation networks has been developed. This model, when integrated with realworld operational environments, is shown to confront issues arising from symmetry and multi-scale features inherent to thermal power distribution room contexts. A combination of multi-scale feature fusion and attention mechanisms has facilitated the precise detection of human-like operation knobs on switch operation panels. To cope with extended dynamic scenes, a method relying on the visual bag-of-words has been adopted, wherein local image features are extracted and matched against a visual dictionary, resulting in a refined visual representation. The subsequent selection of consecutive symmetrically similar scene keyframes and the elimination of superfluous data have been observed to augment the efficacy of loop-closure detection. Such enhancements have culminated in improved accuracy in the SLAM (Simultaneous Localization and Mapping) of mobile robots, enabling their autonomous navigation to designated switching operation task locations. Experimental findings underscore the superiority of this optimized model over traditional semantic segmentation networks, with its ability to pinpoint operation knobs on electrical control cabinet panels in distribution rooms. Moreover, before initiating grasping actions under the Eye-in-hand architecture, visual servo grasping maneuvers can be executed, irrespective of the target's appearance angle within the field of view. This optimization offers an insightful foundation for potential integrations into patrol operation mobile robots, marking a feasible and effective stride forward.

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“…However, due to the complex semantic information present in urban high spatial resolution remote sensing images, roads, buildings, and building shadows are prone to misclassification as water bodies. Furthermore, a major challenge with the water index method lies in determining the appropriate threshold, and improper selection of the threshold can significantly impact the accuracy of water extraction results [10], [11]. Automatic binarization algorithms like OTSU [12] is commonly used in image thresholding, but it is inappropriate to apply a local optimal threshold to remote sensing images of different regions at different times [13].…”

Section: Introductionmentioning

confidence: 99%

AEDNet: An Attention-Based Encoder–Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images

Song,

Rui,

2024

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

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Accurate water extraction from urban remote sensing images holds great significance in assisting the formulation of river and lake management policies and ensuring the sustainable development of urban water resources. However, urban high-resolution remote sensing images encompass complex spatial and semantic information, which leads to disparities between the extracted water body features based on local and global information, consequently affecting the accuracy of urban water extraction. To tackle this issue, an attention-based encoder-decoder network (AEDNet) was proposed. In this network, the backbone employing atrous convolution facilitated the acquisition of low-level and high-level features of urban remote sensing images at various scales. Integrated with the attention mechanism, the encoderdecoder structure extracted global features in both the spatial and channel domains. Subsequently, these two types of features were merged to yield the urban water segmentation. Moreover, considering both intersection over union (IoU) and class weights, a joint loss function was introduced to further enhance the accuracy of urban water extraction. Experimental results demonstrated the strong performance of the proposed method on both GID and LoveDA datasets.

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Spectral index-driven FCN model training for water extraction from multispectral imagery

Cited by 18 publications

References 54 publications

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Semantic Segmentation Optimization in Power Systems: Enhancing Human-Like Switching Operations

AEDNet: An Attention-Based Encoder–Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images

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