Abstract
Improving Remote Sensing Analysis examines how remote sensing data is classified and offers various ways to improve the processs precision and effectiveness. It is emphasized how crucial remote sensing analysis is in a number of domains, including resource mapping and environmental monitoring. The strengths and weaknesses of three classification techniquessupport vector machine, logistic regression, and random forestare thoroughly examined. Furthermore, three approaches to spatial-spectral categorization are presented: pixel-based, object-based, and hybrid. These techniques successfully capture the intricate spatial and spectral properties of remote sensing data by analyzing a dataset. This discovery is important because it will improve remote sensing analysis capabilities, allowing for accurate and rapid information extraction for various applications. This essay highlights the usefulness of classifying remote sensing data and the room for improvement to improve environmental monitoring, resource mapping, and other relevant topics.
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