Eid Ibrahim, N., Hussain Alrawi, A., Abdulhameed, M. (2025). Automated Management and Visualization of Deep Learning-based Image Segmentation for Remote Sensing Data. , 19(1), 233-247. doi: 10.37652/juaps.2024.150377.1265
Noor Eid Ibrahim; Azmi T. Hussain Alrawi; Muthanna M. Abdulhameed. "Automated Management and Visualization of Deep Learning-based Image Segmentation for Remote Sensing Data". , 19, 1, 2025, 233-247. doi: 10.37652/juaps.2024.150377.1265
Eid Ibrahim, N., Hussain Alrawi, A., Abdulhameed, M. (2025). 'Automated Management and Visualization of Deep Learning-based Image Segmentation for Remote Sensing Data', , 19(1), pp. 233-247. doi: 10.37652/juaps.2024.150377.1265
Eid Ibrahim, N., Hussain Alrawi, A., Abdulhameed, M. Automated Management and Visualization of Deep Learning-based Image Segmentation for Remote Sensing Data. , 2025; 19(1): 233-247. doi: 10.37652/juaps.2024.150377.1265

Automated Management and Visualization of Deep Learning-based Image Segmentation for Remote Sensing Data

Journal of University of Anbar for Pure Science
Volume 19, Issue 1 - Serial Number 19, May and June 2025, Pages 233-247    PDF (1.27 M)
Document Type: Research Paper
DOI: 10.37652/juaps.2024.150377.1265
Authors
Noor Eid Ibrahim* 1; Azmi T. Hussain Alrawi1; Muthanna M. Abdulhameed2
1College of Computer Science and IT, University of Anbar, Ramadi, Iraq
2College of Engineering, University of technology, Baghdad, Iraq
Abstract
Received: / /2023
Accepted: / / 2023
Available online: / / 202
DOI:

In this study, we proposed an innovative framework that can automatically manage and visualize the deep learning-based image segmentation output with limited supervision in remote sensing applications. The framework was constructed by integrating recent advances in machine learning models and sophisticated visualization techniques to resolve the problems of scalability and accessibility in processing remote sensing images. We illustrated our methodology with a series of steps, which included preprocessing of remote sensing images, dividing the remote sensing images logically, converting the image data into greyscale images, and developing clustering models such as K-means and Self-Organizing Maps to cluster images into logical groups through the same regular pixel intensity. Two primary deep learning architectures were then implemented, which included Convolutional Neural Network and a hybrid LSTM-GRU that was designed specifically to process image data in a fast and effective manner. The CNN achieved a loss value of 0.015308, MAE of 0.083680, MSE of 0.015308, and RMSE of 0.135449, indicating that the model provides accurate image reconstruction. The LSTM-GRU model developed concluded a slightly higher loss of 0.015364, MAE of 0.076740, MSE of 0.015364, and RMSE of 0.151264, indicating that it can preserve spatial hierarchies and contextual understanding due to slight performance variability. The two models demonstrated excellent processing capabilities that provide evidence on how they can be utilized in urban planning, environmental monitoring, and natural resources management.
Keywords
Remote Sensing Images; Image Segmentation Technique; Clustering; CNN; Automated Image Management
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