Optimum feature selection for recognizing objects from satellite imagery using genetic algorithm

Other Title(s)

اختيار الخواص الأمثل للأجسام الملتقطة من الأقمار الصناعية للتعرف عليها باستخدام الخوارزميات الجينية

Dissertant

al-Ashqar, Iyad Ahmad Abd al-Latif

Thesis advisor

Hwayhi, Nabil Mahmud

Comitee Members

al-Attar, Ashraf Muhammad
Zaqqut, Ihab Salah al-Din

University

Islamic University

Faculty

Faculty of Information Technology

University Country

Palestine (Gaza Strip)

Degree

Master

Degree Date

2014

English Abstract

Object recognition is a research area that aims to associate objects to categories or classes.

Usually recognition of object specific geospatial features, as building, tree, mountains, roads, and rivers from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS).

Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy and reduce data dimensionality.

This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain.

This makes feature selection as an indispensable task in classification task.

In our work, we propose wrapper approach based on Genetic Algorithm (GA) as an optimization algorithm to search the space of all possible subsets related to object geospatial features set for the purpose of recognition.

GA is wrapped with three different classifier algorithms namely neural network, k-nearest neighbor and decision tree J48 as subset evaluating mechanism.

The GA-ANN, GA-KNN and GA-J48 methods are implemented using the WEKA software on dataset that contains 38 extracted features from satellite images using ENVI software.

The proposed wrapper approach incorporated the Correlation Ranking Filter (CRF) for spatial features to remove unimportant features.

Results suggest that GA based neural classifiers and using CRF for spatial features are robust and effective in finding optimal subsets of features from large data sets.

Main Subjects

History and Geography
Information Technology and Computer Science

Topics

• Algorithms
• Geography
• Geographical information systems
• Aerial photography

No. of Pages

81

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-688482