SADA-Net: A Shape Feature Optimization and Multiscale Context Information-Based Water Body Extraction Method for High-Resolution Remote Sensing Images

Wang, Bin; Chen, Zhanlong; Wu, Liang; Yang, Xiaohong; Zhou, Yuan

doi:10.1109/jstars.2022.3146275

Cited by 18 publications

(7 citation statements)

References 79 publications

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“…W. Liu et al [35] conducted research on identifying agricultural land parcels, providing accurate land use information for agricultural management and decision-making. Wang et al [36] developed a method for water extraction, which is crucial for water resource management, environmental monitoring, and disaster response. Faisal et al [37] investigated land cover change, providing important references for urban planning and land management.…”

Section: Discussionmentioning

confidence: 99%

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

Cheng,

Li,

Deng

et al. 2024

Applied Sciences

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High-resolution remote sensing imagery comprises spatial structure features of multispectral bands varying in scale, color, and shape. These heterogeneous geographical features introduce grave challenges to the fine segmentation required for classification applications in remote sensing imagery, where direct application of traditional image classification models fails to deliver optimal results. To overcome these challenges, a multispectral, multi-label model, MMDL-Net, has been developed. This model is integrated with the multi-label BigEarthNet dataset, primarily employed for land cover classification research in remote sensing imagery, with each image composed of 13 spectral bands and spatial resolutions of 10 m, 20 m, and 60 m. To effectively utilize the information across these bands, a multispectral stacking module has been introduced to concatenate this spectral information. To proficiently process three distinct large-scale remote sensing image datasets, a multi-label classification module has been incorporated for training and inference. To better learn and represent the intricate features within the images, a twin-number residual structure has been proposed. The results demonstrate that the MMDL-Net model achieves a top accuracy of 83.52% and an F1 score of 77.97%, surpassing other deep learning models and conventional methods, thereby exhibiting exceptional performance in the task of multispectral multi-label classification of remote sensing imagery.

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Section: Discussionmentioning

confidence: 99%

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

Cheng,

Li,

Deng

et al. 2024

Applied Sciences

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“…In this study, we use RGB images, which can be considered as a low-cost alternative compared to a multispectral image acquisition. However, although RGB images are easier and cheaper to collect, multispectral data may improve performance, particularly the Near-Infrared band, as it has shown an increased performance using CNN-based models [ 54 ]. Regardless of the differences between CNN and ViT models (such as SAM), adding multispectral images to future foundation models may increase the performance of water segmentation.…”

Section: Discussionmentioning

confidence: 99%

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

Trujillano,

Jimenez,

Manrique

et al. 2024

Int J Health Geogr

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Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

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“…Additionally, the symbols y and ŷ signify the ground truth of the segmentation and the corresponding predictions, respectively. Furthermore, to enhance the network's ability to extract boundary information, we utilize the boundary loss (BF1) [55] as the shape stream's loss function, which has demonstrated its effectiveness in RS image semantic segmentation tasks [30], [53], [56]. The shape stream loss (L SS ) consists of the following calculation steps:…”

Section: E Loss Functionmentioning

confidence: 99%

Swin Transformer Embedding Dual-Stream for Semantic Segmentation of Remote Sensing Imagery

Zhou,

Gong

et al. 2024

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

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The acquisition of global context and boundary information is crucial for the semantic segmentation of remote sensing (RS) images. In contrast to convolutional neural networks (CNNs), transformers exhibit superior performance in global modeling and shape feature encoding, which provides a novel avenue for obtaining global context and boundary information. However, current methods fail to effectively leverage these distinctive advantages of transformers. To address this issue, we propose a novel single encoder and dual decoders architecture called STDSNet, which embeds the Swin transformer into the dual-stream network for semantic segmentation of RS imagery. The proposed STDSNet employs the Swin transformer as the network backbone in the encoder to address the limitations of CNNs in global modeling and encoding shape features. The dual decoder comprises two parallel streams, namely the global stream and the shape stream. The global stream utilizes the global context fusion module (GCFM) to address the loss of global context during upsampling. It further integrates GCFMs with skip connections and a multiscale fusion strategy to mitigate large-scale regional object classification errors resulting from similar features or shadow occlusion in RS images. The shape stream introduces the gate convolution module (GCM) to filter out irrelevant features, allowing it to focus on processing boundary information, which improves the semantic segmentation performance of small targets and their boundaries in RS images. Extensive experiments demonstrate that STDSNet outperforms other state-of-the-art methods on the ISPRS Vaihingen and Potsdam benchmarks.

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SADA-Net: A Shape Feature Optimization and Multiscale Context Information-Based Water Body Extraction Method for High-Resolution Remote Sensing Images

Cited by 18 publications

References 79 publications

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

Swin Transformer Embedding Dual-Stream for Semantic Segmentation of Remote Sensing Imagery

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