Optimum Feature Selection For Classfication of Lidar Data Using Genetic Algorithms



Figure 3.Colored Aerial Image & NDDI Image



Figure 4. Intensity Image & Intensity Image last


5. our work
The classification process is composed of the following steps:

Step1: Preparation data, Contain Co-registration of LIDAR and Aerial Image, Noise reduction and filtering the LIDAR data

Step2: generate pool of possible solution
Step3: Optimum feature selection

An optimum feature subset selection has demonstrated in this following diagram:



Step4 .After the selection process, the all image classified with optimum feature subset with ML Classifier.

5.1. Objective Function
A goal of supervised image classification is classify image to a certain class with a highest accuracy and the feature set is optimal that make available this condition.

With this concept The fitness evaluation is a mechanism used to determine the confidence level of the optimized solutions to reach higher accuracy.

In this work we use confusion matrix for accuracy assessment. And extract Kappa parameter that can be computed from below

..............(3)


Maximum value of kappa parameter equal to 1 and Because of this type of genetic algorithm minimized the fitness value, so fitness function defined as

.................(4)


5.2. Parameter Setting
Our experiment used the following parameter setting for genetic algorithm.

  • Initial Population size : 150
  • Population Size: 15
  • Number of generation: 70
  • Crossover: 0.8%
  • Mutation: 0.2%
  • Elite Count: 1
This setting is obtained from several running Genetic program.

6. Experiment and Result
The main goal of these experiments is to optimize the feature set presented in feature space section to reduce the complexity of pattern recognition and increase the overall accuracy of this problem. Above concept has been implemented in MATLAB7.1.

The classification result shows in figure (4), figure (5)


Figure 5. Reconition Result of Tree and Road Class



Figure 6.Recognition result for Grassland and Building


During the analysis of classification results, quality assessment was performed by comparing overall accuracy and kappa coefficient. In general, classification with optimum features leads to overall accuracy of about 0.928%. The result shows that overall accuracy is 3% higher than using all of the features. Furthermore improvement of the accuracy in building class is better than Grass Land class. Table (1) shows the Confusion matrix of image classification with optimum feature subset.

Table 1.Result of ML classifier with test data


7. Conclusion
We have presented the results of applying ML Classification technique on LIDAR data and Aerial Image for 3Dand 2D object recognition. The result shows the capability of using this dataset simultaneously. Furthermore shows that optimum feature subset lead to improvement of classification accuracy.

8. References

  1. M. Sebban, R. Nock; A hybrid filter/wrapper approach of feature selection using information theory Pattern Recognition 35 (2002) 835–846
  2. Amin P. Charaniya, Roberto Manduchi, and Suresh K. Lodha University of California, Santa Cruz "Supervised Parametric Classification of Aerial LiDAR Data
  3. M. Pei1, E. D. Goodman1, W. F. Punch’Feature Extraction Using Genetic Algorithms
  4. Ping Zhang*, Brijesh Verma , Kuldeep Kumar ,2004,“Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection”
  5. Michael L. Raymer, William F. Punch, Erik D. Goodman, Leslie A. Kuhn, and Anil K. Jain “Dimensionality Reduction Using Genetic Algorithms” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 4, NO. 2, JULY 2000
  6. Baranidharan Raman Thomas R.Ioerger Department of Computer Science Texas A&M University “Instance Based Filter for Feature Selection ” Journal of Machine Learning Research 1 (2002) 1-23
  7. Erick Cant´u-Paz ,Shawn Newsam,Chandrika Kamath “Feature Selection in Scientific Applications ” Center for Applied Scientific Computing Lawrence Livermore National Laboratory
  8. F. Samadzadegan, Automatic 3D object recognition and reconstruction based on artificial intelligence and information fusion concepts, University of Tehran (2002).
  9. R.O. Duda and P.E. Hart. Pattern classification and scene analysis. John Wiley & Sons. NY. 1973.
  10. Tuceryan Mihran, Anil K. Jain “Texture Analysis” The Handbook of Pattern Recognition and Computer Vision (1998)

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