Wrapper Feature Selection based on Genetic Algorithm for Recognizing Objects from Satellite Imagery

Abstract

Object recognition is a research area that aims to associate objects to categories or classes. The recognition of object specific geospatial features, such as roads, buildings 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 this work, the authors propose a new approach that combines Genetic Algorithms (GA) with Correlation Ranking Filter (CRF) wrapper to eliminate unimportant features and obtain better features set that can show better results with various classifiers such as Neural Networks (NN), K-nearest neighbor (KNN), and Decision trees. The approach is based on 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.