Hybrid adaptive neural network for remote sensing image classification

Natya Sathyanarayana, Seema Singh


The proposed study employed a method for identifying the main contents (category/class) that a remote sensing image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution (PDD) and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward artificial neural network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.


Histogram; Imaging classification; Neural network; Pixel density distribution; Remote sensing

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DOI: http://doi.org/10.11591/ijai.v13.i2.pp2291-2300


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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