ANALYZING RETRIEVAL METHOD USING CLASSIFICATION AND CLUSTERING PROCESS OF REMOTE SENSOR SATELLITE IMAGES

Abstract

Today, remote sensors and satellite images are utilized across enormous regions. As visual tools, they are very important in cities, agriculture, disaster relief, and tracking systems. Pattern recognition, classification, and clustering are examples of machine learning techniques that can be used to acquire and retrieve the images. This is because they can accurately categorize and classify the images into clusters, which will help search engines discover the image that was given as a question. In our study, we look at Naïve Bayes, SVM Linear and Non-Linear, and Random Forest as classification methods, and Agglomerative Hierarchy and BIRCH as clustering methods. In this study, the fast recovery method and high accuracy rate of image retrieval by classifiers from machine learning were compared with clustering techniques from transfer learning. Using the UC Merced dataset, this research demonstrates that the accuracy classifier as SVM linear is 64.6%, SVM non-linear is 77.7%, random forest is 66.8%, and naïve bayes is 60.3%, and clustering of Agglomerative is 96.5% and BIRCH is 92.8%. The work that was mentioned is one of a kind because the clustering methods work better than the classification methods when they are used. It's unsupervised learning whose scores are above 90% for all classes.