Review of Various Learning Algorithms Applied to Satellite Image Classification

• Published in: 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART) pp. 285 - 289
• Main Authors: Gupta, Mohan Vishal, Dwivedi, Rakesh Kumar, Kumar, Anil
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.12.2021
• Subjects: Back Propagation Neural Network; Classification algorithms; Convolutional Neural Network; Convolutional neural networks; Feed Forward Neural Networks; Recurrent Neural Network; Recurrent neural networks; Resource management; Satellites; Statistical learning; Support Vector Machine; Support vector machine classification
• ISBN: 9781665439688; 1665439688
• ISSN: 2767-7362
• DOI: 10.1109/SMART52563.2021.9676215

Satellite image classification plays a vital role for extracting and analyzing information in satellite images. It is a process that classify an image according to its characteristics. The classification is procedure of recognizing various information and patterns from satellite imagery. In any remote sensing study, the decision- making mainly based on efficiency of the classification process. Image or data classification is a complicated process. This process can be affected by many factors. This paper will review about various classification method used for classifying the satellite images. Paper covers major remote sensing image classification techniques such as Support vector machine (SVM), C-means, K-means, Convolutional Neural Network (CNN) and Recurrent Neural Network ( RNN), Random forest (RF), K-nearest neighbor (KNN), regression, Back propagation neural network (BPNN), Feed forward neural networks, Fuzzy classification. The paper covers detailed overview of various classification methods. More research is required to improve classification.