Innovative Hybrid Techniques for Cloud Detection and Segmentation Using Computer Vision and Machine Learning

Abstract

Cloud detection and segmentation play a critical role in satellite imagery analysis and environmental monitoring. This paper presents a novel hybrid approach that integrates traditional computer vision techniques with advanced machine learning algorithms to enhance both accuracy and efficiency in cloud detection systems. The hybrid methods incorporate image processing techniques such as HSV thresholding, morphological operations, histogram equalization, and Canny edge detection, alongside ensemble learning models like Random Forest, SVM, K-Means clustering, and XGBoost. These hybrid approaches outperform standard methods both in terms of accuracy and computational efficiency, with some hybrid methods offering up to 15% higher accuracy and 70% faster processing times compared to their standard counterparts. These findings highlight the potential of hybrid techniques to significantly improve real-time cloud detection performance.