HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images

Li, Haojin; Wang, Liejun; Cheng, Shuli

doi:10.3390/s22124626

Cited by 3 publications

(1 citation statement)

References 64 publications

(80 reference statements)

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“…U-Net is a deep learning architecture designed for image segmentation tasks, and its name comes from the shape of the network structure, which presents a symmetrical U shape. This network structure is widely used in the field of image segmentation due to its unique design [41][42][43][44]. U-Net introduces jump connections and upsampling paths, which makes it better able to capture features at different scales and preserve the detailed information of the input image.…”

Section: Cells Segmentation Proposed Methodsmentioning

confidence: 99%

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

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Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope. Processing and analyzing images is a time-consuming and laborious task for researchers, as it can easily tire the human eyes and lead to errors in judgment. In recent years, deep learning has made significant progress in the field of medical imaging, especially the successful application of introducing the attention mechanism. The attention mechanism, as a key component of deep learning, improves the understanding and interpretation of medical images by giving different weights to different regions of the image, enabling the model to focus more on important features. To address the challenges in FISH image analysis, we combined medical imaging with deep learning to develop the SEAM-Unet++ automated cell contour segmentation algorithm with integrated attention mechanism. The significant advantage of this algorithm is that it improves the accuracy of cell contours in FISH images. Experiments have demonstrated that by introducing the attention mechanism, our method is able to segment cells that are adherent to each other more efficiently.

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Section: Cells Segmentation Proposed Methodsmentioning

confidence: 99%

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Jian,

Song,

Zhang

et al. 2024

Sensors

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Land Cover Mapping in the Amhara Region of Northwest Ethiopia Using Convolutional Neural Networks and Domain Adaptation Techniques

Alemu,

Neigh,

Caraballo-Vega

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2

2023

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Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models—especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.

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HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images

Cited by 3 publications

References 64 publications

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation

Land Cover Mapping in the Amhara Region of Northwest Ethiopia Using Convolutional Neural Networks and Domain Adaptation Techniques

High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2

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