Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Hoang Thi Minh Chau^1,2,3, Tran Thi Ngan^4,*, Nguyen Long Giang⁵, Tran Manh Tuan⁶, Tran Kim Chau⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4915-4937, 2025, DOI:10.32604/cmc.2025.062784 - 19 May 2025

Abstract Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,… More >

Ananthakrishnan Balasundaram^1,2, Ayesha Shaik^1,2,*, Japmann Kaur Banga², Aman Kumar Singh²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362 - 25 April 2024

Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response… More >

Atheer Joudah^1,*, Souheyl Mallat², Mounir Zrigui¹

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4973-4984, 2023, DOI:10.32604/cmc.2023.036483 - 29 April 2023

Abstract This survey paper aims to show methods to analyze and classify field satellite images using deep learning and machine learning algorithms. Users of deep learning-based Convolutional Neural Network (CNN) technology to harvest fields from satellite images or generate zones of interest were among the planned application scenarios (ROI). Using machine learning, the satellite image is placed on the input image, segmented, and then tagged. In contemporary categorization, field size ratio, Local Binary Pattern (LBP) histograms, and color data are taken into account. Field satellite image localization has several practical applications, including pest management, scene analysis, More >

Nguyen Hai Minh¹, Nguyen Tu Trung^2,*, Tran Thi Ngan², Tran Manh Tuan²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311 - 03 April 2023

Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the More >