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

العناوين الأخرى

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

مقدم أطروحة جامعية

al-Ashqar, Iyad Ahmad Abd al-Latif

مشرف أطروحة جامعية

Hwayhi, Nabil Mahmud

أعضاء اللجنة

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

الجامعة

الجامعة الإسلامية

الكلية

كلية تكنولوجيا المعلومات

دولة الجامعة

فلسطين (قطاع غزة)

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2014

الملخص الإنجليزي

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.

التخصصات الرئيسية

تاريخ و جغرافيا
تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الخوارزميات
• الجغرافيا
• نظم المعلومات الجغرافية
• التصوير الجوي

عدد الصفحات

81

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-688482