Abstract
This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural networks reveal useful information from the temporal sequence and they reported power in cluster analysis and dimensionality reduction. In unsupervised learning, no pre classification and pre labeling of the input data is needed. SOFM is one of the unsupervised neural network used for time series prediction .In time series prediction the goal is to construct a model that can predict the future of the measured process under interest. There are various approaches to time series prediction that have been used over the years. It is a research area having application in diverse fields like weather forecasting, speech recognition, remote sensing. Advances in remote sensing technology and availability of high resolution images in recent years have motivated many researchers to study patterns in the images for the purpose of trend analysis
Highlights
Based on the way of learning, all Artificial Neural Networks can be classified in two learning techniques i.e. Supervised Learning and Unsupervised Learning [1]
In terms of Artificial Neural Network, supervised learning uses the target result to guide the formation of the neural parameters such as multi layer perception and unsupervised learning can be used for clustering the fetched data and calculate features inherent to the problem such as the self organizing map
Self organize map can be used for feature detection inherent to the problem, so it is called self organized feature map (SOFM)
Summary
Based on the way of learning, all Artificial Neural Networks can be classified in two learning techniques i.e. Supervised Learning and Unsupervised Learning [1]. A desired output result for each input vector is required when the network is trained and in unsupervised learning, the training of the network is entirely data-driven and no target results for the input data vectors are provided. In terms of Artificial Neural Network, supervised learning uses the target result to guide the formation of the neural parameters such as multi layer perception and unsupervised learning can be used for clustering the fetched data and calculate features inherent to the problem such as the self organizing map. Self organize map uses the clustering of data without knowing the class memberships of the input data. It provides a topology preserving mapping from high dimension space to map units (neurons). Points nearer to each other in the input space are mapped to nearby map units in the self organize map (SOM). The SOM serve as the cluster analyzing tool of high dimension data and SOM has the capability to generalize the data
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