Algorithm for Remote Sensing Image Classification

This algorithm is designed for remote sensing image classification. It processes multiple remote sensing images to obtain more comprehensive information and achieve higher accuracy in classification results. During implementation, users can optimize classification performance by adjusting the number of clusters (k-value) parameter in the k-means algorithm. The core clustering process involves calculating feature vectors from image pixels and iteratively assigning them to the nearest centroid based on Euclidean distance measurement. Additionally, the algorithm can be integrated with other machine learning techniques such as Support Vector Machines (SVM) and Decision Trees to enhance classification accuracy and reliability through ensemble methods. In practical applications, this algorithm demonstrates broad adaptability across various domains including agriculture monitoring, forestry management, and urban planning. Its modular design allows researchers and engineers to easily incorporate custom feature extraction modules or post-processing routines, making it a versatile tool for remote sensing analysis.