Deep Learning Techniques for Effective Feature Recognition, Selection, and Extraction from Complicated Remote Sensing Datasets - Ambedkar, Juvvala Bala; Agarwal , Ajay (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The development of trustworthy systems that enable many options for "Internet of Things" (IoT) and remote sensing photos has been made possible through the application of machine learning models and middleware characteristics. The usage of remote sensing is particularly necessary to collect geographical data on huge proportions. The primary objective of this research is to conduct a study on deep learning techniques for effective feature recognition, selection, and extraction from complicated remote sensing datasets. According to the findings of the study, the area calculated for the class using the ESRI LULC (Land use/ Land Cover) dataset and the RF (Random Forest) classifier were practically identical. Additionally, the RF classifier has the highest accuracy at 92.17 percent.