A new bio-inspired method for remote sensing imagery classification

TL;DR

This paper introduces a novel bio-inspired neural network approach for satellite image classification, combining radial basis functions with growing neural gas to improve accuracy in remote sensing imagery analysis.

Contribution

It presents a new hybrid neural network method that enhances satellite image classification by integrating radial basis functions with a growing neural gas classifier.

Findings

Improved classification accuracy demonstrated on numeric remote sensing data.

Effective classification of satellite images of Oran city, Algeria.

Enhanced neural network architecture for remote sensing applications.

Abstract

The problem of supervised classification of the satellite image is considered to be the task of grouping pixels into a number of homogeneous regions in space intensity. This paper proposes a novel approach that combines a radial basic function clustering network with a growing neural gas include utility factor classifier to yield improved solutions, obtained with previous networks. The double objective technique is first used to the development of a method to perform the satellite images classification, and finally, the implementation to address the issue of the number of nodes in the hidden layer of the classic Radial Basis functions network. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing imagery. Moreover, the remotely sensed image of Oran city in Algeria has been classified using the proposed technique to establish its utility.