Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images in sub-pixel scale using Hopfield Neural Network
Subject Areas : Journal of Radar and Optical Remote Sensing and GIS
Mohammad Hosein Mehrzade Abarghooee
1
(
Ms in GIS&RS,Yazd Branch, Islamic Azad University, Yazd, Iran
)
Ali Sarkargar Ardakani
2
(
GIS&RS Department, Yazd Branch, Islamic Azad University, yazd, Iran
)
Keywords: Super resolution, Land cover, Fuzzy classification, Hopfield Neural Network, Spatial resolution, Subpixel, Energy function,
Abstract :
Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by thepixel.This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function. energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to remote sensing imagery and the resultant maps provided an accurate and improved representation of the land covers. Low RMSE, high accuracy By using a Hopfield neural network, more accurate measures of land cover targets can be obtained, The Hopfield neural network used in this way represents a simple, robust, and efficient technique, and results suggest that it is a useful tool for identifying land cover targets from remotely sensed imagery at the subpixel scale. The present research purpose was evaluation of HNN algorithm efficiency for different land covers (Land, Water, Agricultured land and Vegetation) on MODIS & OLI images and related ranking, results of present super resolution algorithm has shown that according to precedence, most improvement in feature’s recognition happened for Water, Land, Agricultured land and ad last Vegetation with RMSEs 0.044, 0.072, 0.1 and 0.108.
