Time Variant Change Analysis in Satellite Images

This paper describes the time variant changes in satellite images using Self Organizing Feature Map (SOFM) technique associated with Artificial Neural Network. In this paper, we take a satellite image and find the time variant changes using above technique with the help of MATLAB. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying Artificial Neural Networks (ANNs), including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. We first make a brief introduction to models of networks, for then describing in general terms Artificial Neural Networks (ANNs). As an application, we explain the back propagation algorithm, since it is widely used and many other algorithms are derived from it. There are two techniques that are used for classification in pattern recognition such as Supervised Classification and Unsupervised Classification. In supervised learning technique the network knows about the target and it has to change accordingly to get the desired output corresponding to the presented input sample data. Most of the previous work has already been done on supervised classification. In this study we are going to present the classification of satellite images using unsupervised classification method of ANN.

@article{Sharma2013,
  title     = {Time Variant Change Analysis in Satellite Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {4},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Rachita Sharma and Sanjay Kumar Dubey},
  doi       = {10.14569/IJACSA.2013.040420},
  url       = {https://doi.org/10.14569/IJACSA.2013.040420}
}