An enhanced neural network based approach towards object extraction

TL;DR

This paper presents a neural network-based method for automatic object extraction from satellite images, improving efficiency and accuracy by considering multiple object features and allowing flexible identification within categories.

Contribution

It introduces a neural network approach that combines spectral, shape, and textural features for enhanced object extraction in satellite imagery, with verified effectiveness.

Findings

High accuracy verified by ground truth

Effective extraction of objects based on multiple features

Flexible identification within object categories

Abstract

The improvements in spectral and spatial resolution of the satellite images have facilitated the automatic extraction and identification of the features from satellite images and aerial photographs. An automatic object extraction method is presented for extracting and identifying the various objects from satellite images and the accuracy of the system is verified with regard to IRS satellite images. The system is based on neural network and simulates the process of visual interpretation from remote sensing images and hence increases the efficiency of image analysis. This approach obtains the basic characteristics of the various features and the performance is enhanced by the automatic learning approach, intelligent interpretation, and intelligent interpolation. The major advantage of the method is its simplicity and that the system identifies the features not only based on pixel value but also based on the shape, haralick features etc of the objects. Further the system allows flexibility for identifying the features within the same category based on size and shape. The successful application of the system verified its effectiveness and the accuracy of the system were assessed by ground truth verification.