The rapid advancement of machine learning techniques has revolutionized various fields, including remote sensing data analysis. This paper explores the applications of machine learning in the analysis of remote sensing data, highlighting its potential to enhance the accuracy and efficiency of data interpretation. Remote sensing data, collected from satellites and aerial platforms, offer vast amounts of information about Earth's surface, atmosphere, and oceans. However, the complexity of this data can make it challenging to process and extract meaningful insights. Machine learning algorithms have proven to be effective tools for addressing these challenges, by learning from patterns and relationships within the data. This paper discusses several key applications of machine learning in remote sensing, such as image classification, change detection, and object detection. Furthermore, it examines the limitations and future directions of incorporating machine learning techniques into remote sensing data analysis. By demonstrating the capabilities of machine learning, this paper aims to provide insights into how this technology can contribute to the advancement of Earth observation and monitoring.
Martin, M. Machine Learning Applications in Remote Sensing Data Analysis. Transactions on Applied Soft Computing, 2022, 4, 34. https://doi.org/10.69610/j.tasc.20221119
AMA Style
Martin M. Machine Learning Applications in Remote Sensing Data Analysis. Transactions on Applied Soft Computing; 2022, 4(2):34. https://doi.org/10.69610/j.tasc.20221119
Chicago/Turabian Style
Martin, Michael 2022. "Machine Learning Applications in Remote Sensing Data Analysis" Transactions on Applied Soft Computing 4, no.2:34. https://doi.org/10.69610/j.tasc.20221119
APA style
Martin, M. (2022). Machine Learning Applications in Remote Sensing Data Analysis. Transactions on Applied Soft Computing, 4(2), 34. https://doi.org/10.69610/j.tasc.20221119
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