Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis

Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signatures, complex texture and shape, spatial relationships and temporal changes. In this research, we present the performance evaluation and analysis of deep learning approaches based on Convolutional Neural Networks and vision transformer towards achieving efficient classification of remote sensing satellite images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG and InceptionV3. The models were evaluated on three publicly available EuroSAT, UCMerced-LandUse and NWPU-RESISC45 datasets containing categories of images. The models achieve promising results in accuracy, recall, precision and F1-score. This performance demonstrates the feasibility of Deep Learning approaches in learning the complex and in-homogeneous features of the high-resolution remote sensing images.

Automated summary

This research evaluates and analyzes the performance of deep learning approaches, including Convolutional Neural Networks and vision transformer, for classification of high-resolution satellite images. Various CNN-based models were explored and evaluated on publicly available datasets. The results demonstrate the feasibility of Deep Learning approaches in learning the complex features of remote sensing images.