A Novel Depth-Wise Separable Convolutional Model for Remote Sensing Scene Classification

With the advancement in satellite and Artificial Intelligence (AI), the increase in observation of the earth is increasing dramatically. With this development, the demand in the field of Remote Sensing (RS) is also growing rapidly. The spatial resolution and textural information of remote sensing images can be improved by introducing AI and Machine Learning (ML) technology. In the modern era of computer science, Deep Learning (DL) models are more familiar in the field of scene classification. This paper aims to develop a novel depth-wise CNN model to classify the RS images with low time effort during training with higher accuracy than the existing CNN model. For comparison, three typical CNN models of VGG16, VGG19, ResNet50 and RegNet are taken and tested on the RS datasets for classification. The experimented analysis demonstrates that the proposed classification model surpasses the existing classification models by producing higher accuracy in testing by taking a minimum time duration for training the RS datasets.