Optimum Feature Selection For Classfication of Lidar Data Using Genetic Algorithms
F. Samadzadegan
Department of Geomatics Engineering,
Faculty of Engineering, University of Tehran
samadz@ut.ac.ir
A. Javaheri
Department of Geomatics Engineering,
Faculty of Engineering, University of Tehran
a.javahery@yahoo.com
Due to the low resolution characteristic of available commercial LIDAR systems, it becomes difficult to correctly classify objects from LIDAR range data. In order to improve the performance of classification process, additional data should be considered. These are mainly: First and last LIDAR pulse and Intensity of returned laser beam and color Aerial Image. By using different combination of mentioned information, several numbers of features (Pattern Descriptor) have been developed. Nevertheless, there are no theoretical guidelines that suggest the appropriate features to be used in specific classification situations.
The presented method uses a genetic algorithm for feature selection. Genetic algorithms (GAs), a form of inductive learning strategy, are adaptive search techniques which have demonstrated substantial improvement over a variety of random and local search methods
We have taken the most popular classifier, the maximum likelihood classifier, to evaluate the quality of the output of proposed optimum feature selection method. A framework for quality assessment has been proposed and tested, based on similarity measures between classified data and reference data. The numerical investigation of the obtained results demonstrates the high capability of the proposed method for determining the optimum features for classification of LIDAR data. And results show that Image classification with optimum feature subset increase the overall accuracy.
1. Introduction
Recognition and reconstruction of the object in real world is a major goal for many fields of research such as, photogrammetry, machine vision and vision metrology. In this field, we can define an object with textural, structural and spectral property. Textural property related to this fact that, the image of real objects often do not exhibit regions of uniform intensities and textural image, defined as a function of the spatial variation in pixel intensities (Tuceryan 1998). Structural feature describes the geometry of an object and finally the electromagnetic radiation reflected by objects of the same nature is similar overall and these objects will thus have similar spectral property.
Simplest way to classify image is use all of extricable features simultaneously in classification algorithm but and there are a number of inter-related reasons why feature selection is desirable.
- Using a smaller feature set may improve classification accuracy by eliminating noise inducing features (Jain & ongker, 1997, Siedlecki & Sklansky, 1989).
- Small feature sets should be more generalizable to unseen data. If training data is in short supply, the use of a small number of features may reduce the risk of “overfitting” the parameters of a classifier to the training data (Yang & Honavar, 1998).
- The use of a small feature set raises the credibility of the estimated performance of the classifier (Siedlecki & Sklansky, 1989).
“Feature selection” is the process of selecting an optimum subset of features from the enormous set of potentially useful features which may be available in a given problem domain (Gose, Johnsonbough & Jost, 1996). Therefore, the main goal of feature subset selection is to reduce the number of features used in classification while maintaining acceptable classification accuracy or increase it. This process is a very important step in organizing a classifier. Theoretical approach cannot be applied to determine the optimal combination of features, and the only way to select the optimal feature subset is to evaluate all possible combinations of the features.
Feature subset selection algorithms can be classified into two categories. If feature selection is done independently of the learning algorithm, the technique is said to follow a filter approach. Otherwise, it is said to follow a wrapper approach (M. Sebban, 2001). The filter approach is computationally more efficient but its major drawback is that an optimal selection of features may not be independent of the inductive and representational biases of the learning algorithm that is used to build the classifier. On the other hand, the wrapper approach involves the computational overhead of evaluating a candidate feature subset by executing a selected learning algorithm on the database using each feature subset under consideration. wrapper based algorithm is categorized into three, Sequential, Exponential and Random Search. Genetic algorithm is type of randomized search strategy. The applicability of GAs to the optimum feature subset selection problem is obvious, and there has been considerable interest in this area in the last decade. In this paper, genetic algorithms are applied to optimum feature subset selection.