Machine learning books and papers

Forwarded from Papers

با عرض سلام
مقاله ی زیر تماما نگارش شده و اماده سابمیت از دوستان کسی خواست نفرات ۳ و ۴ اش خالی هست.

IEEE Geoscience and Remote Sensing Letter
Impact factor 4
CiteScore 7.6
------------------------------
Title: Enhanced-HisSegNet: An Enhanced Histagram Layered Segmentation Network for SAR Image-based Flood Segmentation
------------------------------
Abstract:
Floods are among the most frequent natural disasters, causing loss of life and significant economic and environmental damage, with direct impacts on agriculture, urban infrastructure, and transportation networks. Therefore, it is crucial to accurately and efficiently identify flooded areas in the aftermath of such events. Synthetic Aperture Radar (SAR) imagery plays a vital role in this process, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model by Turkmenli et al. [1] through the integration of fine-tuned histograms and Deep Neural Networks (DNNs) for improved flood mapping. Specifically, we introduce fine-tuned histogram extraction layers designed for SAR data, which are integrated into Deep Segmentation Neural Networks (DSNNs). The model was tested on two real SAR datasets, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with fine-tuned histogram layers, outperforms previous approaches by up to 4% in intersection over union (IoU) and provides a comprehensive evaluation through metrics such as Precision, Recall, Average Precision (AP), Mean Average Precision (mAP), False Positive Rate (FPR), and Mean Average Recall (mAR). Importantly, these improvements come with minimal additional learnable parameters. The code for this work will be made available at https://github.com/Mohsena1990/Enhanced-HistSegNet

@Raminmousa
@Machine_learn
@Paper4monry

GitHub

GitHub - Mohsena1990/Enhanced-HistSegNet: The code related to HistSegNet approach was submitted to IEEE Geoscience and Remote Sensing…

The code related to HistSegNet approach was submitted to IEEE Geoscience and Remote Sensing Letters. - Mohsena1990/Enhanced-HistSegNet

www.tg-me.com/us/Machine learning books and papers/com.Machine_learn/2891

3.4K viewsSep 23, 2024 at 10:13

با عرض سلام
مقاله ی زیر تماما نگارش شده و اماده سابمیت از دوستان کسی خواست نفرات ۳ و ۴ اش خالی هست.

IEEE Geoscience and Remote Sensing Letter
Impact factor 4
CiteScore 7.6
------------------------------
Title: Enhanced-HisSegNet: An Enhanced Histagram Layered Segmentation Network for SAR Image-based Flood Segmentation
------------------------------
Abstract:
Floods are among the most frequent natural disasters, causing loss of life and significant economic and environmental damage, with direct impacts on agriculture, urban infrastructure, and transportation networks. Therefore, it is crucial to accurately and efficiently identify flooded areas in the aftermath of such events. Synthetic Aperture Radar (SAR) imagery plays a vital role in this process, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model by Turkmenli et al. [1] through the integration of fine-tuned histograms and Deep Neural Networks (DNNs) for improved flood mapping. Specifically, we introduce fine-tuned histogram extraction layers designed for SAR data, which are integrated into Deep Segmentation Neural Networks (DSNNs). The model was tested on two real SAR datasets, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with fine-tuned histogram layers, outperforms previous approaches by up to 4% in intersection over union (IoU) and provides a comprehensive evaluation through metrics such as Precision, Recall, Average Precision (AP), Mean Average Precision (mAP), False Positive Rate (FPR), and Mean Average Recall (mAR). Importantly, these improvements come with minimal additional learnable parameters. The code for this work will be made available at https://github.com/Mohsena1990/Enhanced-HistSegNet

@Raminmousa
@Machine_learn
@Paper4monry

BY Machine learning books and papers