Mehrzadeh Abarghooee, M., Sarkargar Ardakani, A. (2018). Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images at sub-pixel scale using Hopfield Neural Network. Journal of Radar and Optic Remote Sensing, 1(1), 36-57.
Mohammad Hosein Mehrzadeh Abarghooee; Ali Sarkargar Ardakani. "Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images at sub-pixel scale using Hopfield Neural Network". Journal of Radar and Optic Remote Sensing, 1, 1, 2018, 36-57.
Mehrzadeh Abarghooee, M., Sarkargar Ardakani, A. (2018). 'Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images at sub-pixel scale using Hopfield Neural Network', Journal of Radar and Optic Remote Sensing, 1(1), pp. 36-57.
Mehrzadeh Abarghooee, M., Sarkargar Ardakani, A. Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images at sub-pixel scale using Hopfield Neural Network. Journal of Radar and Optic Remote Sensing, 2018; 1(1): 36-57.
Evaluation of super-resolution algorithm for detection and recognition of features from MODIS and OLI images at sub-pixel scale using Hopfield Neural Network
1Ms in GIS&RS,Yazd Branch, Islamic Azad University, Yazd, Iran
2GIS&RS Department, Yazd Branch, Islamic Azad University, yazd, Iran
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 the pixel. Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. 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. This 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 (MODIS & OLI images), 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 sub-pixel scale. The present research purpose was evaluation of HNN algorithm efficiency for different land covers (Land, Water, Agriculture land and Vegetation) through Area Error Proportion, RMSE and Correlation coefficient parameters 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, Agriculture land and ad last Vegetation with RMSEs 0.044, 0.072, 0.1 and 0.108.
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