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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ENHANCED WATER BODY SEGMENTATION VIA HIERARCHICAL NEURAL NETWORKS
Authors
Siddineni Sasi Priyanka,Chekuri Subha Keerthi,Mamilla Gowtham,Telu Veera Chaitanya,Dr srinivas kumar palvadi
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Keywords
Water Body Segmentation System, Satellite Image Analysis, Feature Detection, Image Processing, Performance Evaluation, Remote Sensing, Data Analysis.
Abstract
Segmentation of water bodies from remote sensing satellite images is crucial for processing and
analysis. Efficiently extracting water body features is essential for various real-time applications
such as water resource allocation, ecological services assessment, monitoring systems for water
resource protection, and identification of flooding disasters. The interpretation of satellite images
and the random extraction of water bodies are emerging tasks in remote sensing. Convolutional
Neural Networks (CNNs) have shown promise in various remote sensing applications, including
efficient segmentation of water bodies from satellite images. Several CNN-based approaches have
been proposed for this purpose. Multi-scale Water Extraction CNN (MWEN) stands out as a
particularly effective method for segmenting water from Geo-Fan-2 (GF-2) sensing satellite
images. However, addressing the automatic extraction of water body segmentation from satellite
images remains challenging due to the sparse arrangement of boundary pixels and the large number
of training samples required. To enhance the performance of automatic water body segmentation
from satellite images, we propose a Novel Optimized Multi-Feature Contour-based Hierarchical
Neural Network (NOMFCHNN). NOMFCHNN incorporates expanding neural network features
and layers inspired by inception models, which provide crucial information about network
localization. This approach employs pixel matching with extended feature extraction. Extensive
experiments on combined satellite image repositories demonstrate that our proposed approach
improves segmentation accuracy and other key parameters compared to state-of-the-art methods
for remote sensing satellite images.
IJCRT's Publication Details
Unique Identification Number - IJCRT24A4884
Paper ID - 258135
Author type - Indian Author
Page Number(s) - q375-q384
Pubished in - Volume 12 | Issue 4 | April 2024
DOI (Digital Object Identifier) -
No Of Downloads - 47
Author Country - India, 521108, vijayawada, vijayawada, 521108, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Siddineni Sasi Priyanka,Chekuri Subha Keerthi,Mamilla Gowtham,Telu Veera Chaitanya,Dr srinivas kumar palvadi, "ENHANCED WATER BODY SEGMENTATION VIA HIERARCHICAL NEURAL NETWORKS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.q375-q384, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A4884.pdf
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