Semantic Segmentation of Land Cover in Urban Areas by Fusing Multisource Satellite Image Time Series

Yan, Jining; Liu, Jingwei; Liang, Dong; Wang, Yi; Li, Jun; Wang, Lizhe

doi:10.1109/tgrs.2023.3329709

Cited by 6 publications

(1 citation statement)

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“…Inspired by the residual network [32], a shortcut connection was embedded into block 1 to add low-level feature 1 to the new feature map. This addition maximally preserves the low-level information crucial for distinguishing inter-class difference [33], as these features are primarily obtained from the true color images [34]. Apart from the shortcut connection operation, Block 2 mirrors the structure of Block 1.…”

Section: Designing the Dednetmentioning

confidence: 99%

DEDNet: Dual-Encoder DeeplabV3+ Network for Rock Glacier Recognition Based on Multispectral Remote Sensing Image

Lin,

Liu,

Liu

et al. 2024

Preprint

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Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the periglacial environment. Deep learning algorithms and red-green-blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from QiLian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (Mean Intersection over Union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer's accuracy of 93.68% and a user's accuracy of 94.18%. Furthermore, the model was employed to two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

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Section: Designing the Dednetmentioning

confidence: 99%