Multiclass classification of remote sensing images using deep learning techniques

Deep learning has strong learning ability to extract the features from Image datasets. In recent years, deep networks especially deep convolutional neural networks have revolutionized this field. When exposed to a huge number of datasets and their labels, deep learning techniques like Convolutional Neural Networks (CNNs) can produce precise categorization results. However, employing CNNs with scant labeled data might have a number of issues, including the problem of heavy overfitting. Convolutional Neural Networks are the backbone of modern deep learning architectures for the purpose of image classification. We solved image classification problem with different architectures and compare their performances. The objective of this work lies in the approach to study the given datasets. We use domain adaptation approach to highlight the underlying characteristics of these datasets and the different parameters (activation function, weights, regularization, neural simulation etc.) associated with these architectures. In our work, we use three datasets-RS19, UC Merced, and EuroSat images-were utilized in the CNN implementation to training the suggested model. The obtained results effectively demonstrate the local representation capacity of CNNs. Furthermore, this work shows that transfer learning improves classification outcomes in optical remote sensing images, particularly when the training sample is small.