Abstract
The classifying process of Remote Sensing Image (RSI) has been crucial in diverse applications, involving land cover mapping, environmental monitoring, disaster assessment, and urban planning. Conventionally, image classification in Remote Sensing (RS) relies on Machine Learning (ML) and handcrafted feature extraction approaches. But, lately, Deep Learning (DL) has become an effective and powerful method for RSI classification. DL algorithms, especially Convolutional Neural Network (CNN), have shown great performance in different Computer Vision (CV) tasks, involving image classification. This study proposes a Robust RSI Classification using Multiverse Optimization Algorithm with Deep Transfer Learning (RRSIC-MVO) model. The RRSIC-MVO technique exploits the DL with hyper-parameter selection mechanism to classify the RSI. In the presented RRSIC-MVO technique, the noise occurrence can be handled by joint bilateral filter (JBF). Following, MobileNetv3 model is applied for feature extractor and its hyper-parameters can be elected by the use of FA. Finally, soft-margin support vector machine (SM-SVM) model is applied for classification purposes. A series of investigations are accomplished on RS dataset for assessing the RRSIC-MVO model. The outputs demonstrate the effectiveness of RRSIC-MVO technique, outperforming traditional machine learning methods.
Keywords: Classification, Computer Vision, Deep Learning, Multiverse Optimization Algorithm, Remote Sensing Images.