1 / 33

Stiijn Peeters University of Antwerp, Antwerp, Belgium Dr. Prashanth Marpu Mattia Pedergnana

Classification Using Extended Morphological Attribute Profiles Based On Different Feature Extraction Techniques. Stiijn Peeters University of Antwerp, Antwerp, Belgium Dr. Prashanth Marpu Mattia Pedergnana Prof. Jon Atli Benediktsson University of Iceland, Reykjavik, Iceland

rafi
Download Presentation

Stiijn Peeters University of Antwerp, Antwerp, Belgium Dr. Prashanth Marpu Mattia Pedergnana

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Classification Using Extended Morphological Attribute Profiles Based On Different Feature Extraction Techniques Stiijn Peeters University of Antwerp, Antwerp, Belgium Dr. Prashanth Marpu Mattia Pedergnana Prof. Jon Atli Benediktsson University of Iceland, Reykjavik, Iceland Dr. Mauro Dalla Mura Prof. Lorenzo Bruzzone University of Trento, Trento, Italy

  2. Overview Background Morphological Attribute Profiles Classification of Hyperspectral data Ongoing work Conclusions

  3. Background Morphological profiles (MP) and Morphological attribute profiles (MAP) have been successfully used to fuse spectral and spatial information for the classification of remote sensing data. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480–490, Mar. 2005 M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, ”Morphological Attribute Profiles for the Analysis of Very High Resolution Images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747–3762, Oct. 2010

  4. Background Traditionally, MPs and MAPs are built using the feature extraction based on principal component analysis (PCA). Moreover, the selection of filter parameters is traditionally done manually. In this study, We analyse the classification results by using various feature extraction techniques (PCA, Kernel PCA, DAFE, DBFE). We use a simple method to build the MAPs based on standard deviation attribute automatically.

  5. Morphological Profile (MP) and Morphological Attribute Profile (MAP)

  6. Morphological Profiles When dealing with real images it is difficult to identify a single filter parameter suitable to handle all the objects in the image. Perform a multilevel analysis by using several values for the filter parameters. Build a stack of images with different degrees of filtering. Morphological Profile (MP) M. Pesaresi and J. A. Benediktsson, “A new approach for the morphological segmentation of high-resolution satellite imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 2, pp. 309-320, 2001. 6

  7. Morphological Profiles Morphological Profiles (MPs) are composed by a sequence of opening and closing with SE of increasing size. Closing Profile Opening Profile MP Square SE Sizes: 7, 13, 19, 25 7

  8. Extended Morphological Profile Morphological profile 1 X1 X1 F1 MP X1 X1 X1 X1 X1 F2 X1 X1 MP X1 X Feature Reduction Hyperspectral Image Extended Morphological Profile X1 Fn X1 MP Morphological profile n

  9. Attribute Profiles Thinning Profile Thickening Profile Square SE (MP) Sizes: 7, 13, 19 Area Attribute λ: 45, 169, 361 Criterion: Area > λ Moment of Inertia Attribute λ: 0.2, 0.1, 0.3 Criterion: Inertia > λ STD Attribute λ: 10, 20, 30 Criterion: STD > λ 9

  10. Selection of thresholds for constructing MAP In this study, we only use the attribute profile generated using the standard deviation attribute. The thresholds to build the profile are estimated for every feature separately from the range of standard deviation values of the training samples of all the classes. So, different threshold values are used for diferent profiles. A more general approach to use a big range of attributes has been recently proposed. An entire profile using a wide range of attributes and wide range of thresholds is built and a newly proposed hybrid genetic algorithm is used for feature selection. Master Thesis: Mattia Pedergnana (University of Iceland, Iceland and University of Trento, Italy) Optimal Automatic Construction of Morphological Profiles

  11. Results

  12. Data used ROSIS University of Pavia

  13. Data used AVIRIS Indian Pine

  14. Data used ROSIS University of Pavia AVIRIS Indian Pines

  15. Results: University of Pavia data PCA SVM OA 92.01% AA 92.17% k 0.8957 Random Forest OA 91.31% AA 87.96% k 0.8894

  16. Results: University of Pavia data Kernel PCA RF OA 92.2% AA 95.02% k 0.8993 SVM OA 92.31% AA 93.96% k 0.9002

  17. Results: University of Pavia data DAFE RF OA 96.25% AA 96.28% k 0.951 SVM OA 92.69% AA 93.27% k 0.9119

  18. Results: University of Pavia data DBFE RF OA 95.09% AA 95.32% k 0.9386 SVM OA 93.45% AA 94.16% k 0.9145

  19. Summary of Results University of Pavia Extended Attribute Profile using Standard deviation

  20. Extended Morphological Profiles Params: Initial Size: 1 Step: 2 Number Of Opening/Closing: 3

  21. Results: Indian Pine PCA RF OA 92.20% AA 95.32% k 0. 9100 SVM OA 88.94% AA 92.96% k 0.8738

  22. Results: Indian Pine Kernel PCA RF OA 92.87% AA 96.01% k 0.9183 SVM OA 88.93% AA 93.36% k 0.8737

  23. Results: Indian Pine DAFE RF OA 77.21% AA 87.62% k 0.7427 SVM OA 68.70% AA 71.31% k 0.6464

  24. Results: Indian Pine DBFE RF OA 81.28% AA 87.78% k 0.7866 SVM OA 73.13% AA 79.81% k 0.6954

  25. Summary of Results Indian Pine Extended Attribute Profile using Standard deviation

  26. Extended Morphological Profiles Params: Initial Size: 1 Step: 2 Number Of Opening/Closing: 3

  27. Conclusion • The results of classifying hyperspectral data using morphological attribute filters with various feature extraction techniques has been studied. • Better results are obtained with attribute profiles compared to morphological profiles. • Supervised feature reduction techniques are constrained by the number of available samples and hence do not provide consistent results. • Linear unsupervised feature reduction techniques such as PCA may not be useful as the features are not always able to distinguish between classes effectively. This is observed in the experiments.

  28. Conclusion • However, non-linear techniques such as kernel PCA preserve the information of the independent clusters and hence can be useful in distinguishing between classes. Experiments suggest that EAP with KPCA produces consistent and high quality classification results. • We are currently investigating the results with a wide range of feature extraction techniques. • We are also working on methods to automatically identify the thresholds to build profiles.

  29. Experimental Results Pavia Dataset – Second Approach – HML-DBFE-RF Overall Accuracy: 98.3% Average Accuracy: 98.6% 29

  30. Experimental Results Pavia Dataset – Second Approach – HML-KPCA-RF Overall Accuracy: 90% Average Accuracy: 97 % 30

  31. Experimental Results Pavia Dataset – Second Approach – HML-KPCA-SVM Overall Accuracy: 93.8% Average Accuracy: 96.9 % 31

  32. Experimental Results Indian Pine– Second Approach – HML-KPCA-RF Overall Accuracy: 93.8% Average Accuracy: 96 % 32

  33. Thank You for your attention Software in Matlab can be freely obtained by sending us an email. Prashanth Marpu prashanthmarpu@ieee.org Mattia Pedergnana mattia@mett.it Mauro Dalla Mura dallamura@disi.unitn.it

More Related