José A. Díaz Santos, Graduate Student (UPRM), Raúl E. Torres Muñiz, PhD. (UPRM)

and Roy A. Armstrong, PhD. (UPRM)

Conclusion The classification results in Table .1 show that the high variability in

texture of these RGB underwater coral reefs images cannot be classified using first order statistics. The feature space is not enough for the proper classification of these RGB images, for that reason methods that can increase the parameters in the feature space are needed, and the texture features may be a possible solution to overcome this problem in the classification.

Present and Future Work Classify the difference classes of coral separately, because there some

classes that can be classify easier than others and also include the confusion matrices of all the classes.

Explore different color spaces like HSI,YIQ and XYZ [4] among others, looking for the best discrimination of the classes.

Implementation of the different texture features selection as the statistical approach, the spectral approach and the multiresolution approach.

Combine these new feature selection techniques with spatial characteristics of the sea floor, for improve the classification accuracy of the classification algorithm.

References

[1] J.K Shuttleworth, A.G Todman, R.N.G Naguib, B.M Newman, M.K Bennett, “Colour texture analysis using co-occurrence matrices for classification of colon cancer images” Proc. IEEE CCECE. Canadian Conference on Volume 2, 12-15 May 2002 Page(s):1134 - 1139 vol.2, 2002

[2] Liu Xiuwen and Wang DeLiang, “Texture classification using spectral histograms” IEEE Transactions on Image Processing, Volume 12, Issue 6, Page(s):661 – 670 June 2003.

[3] M. Soriano, S. Marcos, Caesar Saloma, “Image Classification of Coral Reef Components from Underwater Color Video” MTS/IEEE Conference and Exhibition, Volume 2, 5-8 Nov. 2001 Page(s):1008 - 1013 vol.2, 2001.

[4] R.Gonzalez and R. Woods, Digital Image Processing, 2nd ed. New Jersey: Prentice Hall, 2002.

[5] Rivera Maldonado Francisco J., “Segmentation of Underwater Multispectral Images with Applications in the Study of Coral Reefs”, MS Thesis, University of Puerto Rico Mayaguez Campus, Department of Electrical and Computer Engineering, 2004.

[6] R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2rd ed. New York: John Wiley & Sons, Inc., 2001.

[7] Xiaoou Tang and W.K. Stewart, “Texture classification using wavelet packet and Fourier transforms,”IEEE Conference Proceedings. 'Challenges of Our Changing Global Environment'. Volume 1, 9-12 Page(s):387 - 396 vol.1, Oct.1995.

[8] http://soundwaves.usgs.gov/2003/05/

[9] http://www.whoi.edu/DSL/hanu/seabed/

This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821).

Abstract Coral Reefs ecosystems are complex communities in which life

diversity surpass all ecosystems on planet. Moreover, in the last decades these ecosystems have been dying excessively, declining the coral communities worldwide. The objective of this research is the development of a classification algorithm with applications in the study of coral reefs RGB images in order to monitor those communities automatically. The main problem in the classification is that some classes are overlapped (Figure .4), for that reason typical classifiers that used primary order statistics like mean and covariance cannot be used. The classification algorithm developing in this research use the Local Homogeneity Coefficient (LHC) algorithm [5] as first stage to find the different regions of interest in the image like corals, rocks and sand among others classes. Then, using different texture features like spectral features and statistical features the classification of each region will be perform. Finally, the performance and the results of the algorithm will be validated with the manual classification done with the Canvas software (Figure .3). In this research, a set of 100 images are used to test the algorithm. At this moment, some classifiers as Euclidean, neural networks and maximum likelihood [6] failed in the classification, with classification percent around 50% (Table .1).

Introduction One important tool in image analysis that is investigated in CenSSIS

(The Center for Subsurface Sensing and Imaging Systems) is image classification that is the main objective of this work. In this research, an automated underwater vehicle (AUV) (Figure .1) performed optical sensing using a 12-bit 1280x1024 monochrome CCD camera at approximately 30-80 meters depth [8],[9]. These images have low contrast, and are very noisy. Also, they are extremely rich in both spectral variability and texture (Figure .2).

Figure .1 The Automated Underwater Vehicle [8],[9].

In image classification, the acquisition process is a crucial step, especially in image rich in texture variability. This acquisition process is affected by many factors like change in environmental conditions, noise contamination by the water and the sensor, varying illumination, change in view point of the sensor and geometry of the sea bed, converting this process in a random process. For that reason, a robust feature selection method is needed, invariant for those kinds of disturbances, indeed of traditional first order statistics as mean and covariance. At this time, texture features have being studying for further implementation. There are some textures approaches in the literature of image classification as statistical with the use of co-occurrence matrices, spectral with the use of different filters response like Gabor filters and the multiresolution approach with the use of the wavelet transform [1], [2], [3], [4], [6], [7].

Classification of Underwater RGB Images with Applications in the Study of Coral Reefs

Figure .4 Classes Patterns Distributions

The first stage of this research is the implementation of the Local Homogeneity Segmentation algorithm, for image segmentation [5].

Next, using the segmented resulted image (Figure .6) from the first stage, a square of each segment in the image is selected. Feature selection is performed in this squares to perform the further discrimination. The features that will be implemented are texture features using three different approaches as spectral, statistical and Multiresolution [1], [2], [3],[7].

The classification of each segment in the image is performed using the features selected with different classifiers as Maximum Likelihood among others.

The performance of the classification algorithm (Figure .5) is compared with the classification using the Canvas Software (Figure .3).

Figure .2 Underwater original coral reef image

Figure .3 Image classification in Canvas Program

Table 1.Classification Results

Figure .6 Segmented Image

Figure .5 Classification Algorithm

Classifier ResultsWith 100 Images

Mean Percent (True/True)

(False/False)Better Percent Worst Percent

Euclidean (Linear) RGB 56.70% 75.30% 44.20%

Euclidean (Linear) HSI 39.96% 64.28% 23.03%

ML (Quadratic) RGB 49.98% 77.15% 33.09%

ML (Quadratic) HSI 57.34% 73.75% 29.34%

Angle RGB 50.73% 84.53% 31.22%

Angle HSI 38.70% 64.65% 23.17%

Euclidean and Texture RGB

61.92% 78.36% 31.20%

Neural Networks RGB(3 layers 27-27-21)

47.97% 57.49% 37.80%

Objectives

Develop a classification algorithm based on the segmentation of the images.

Evaluation and comparison of different feature extraction and feature selection techniques in order to improve the classification performance of the classifiers.

Evaluate the error percent of the classification algorithm by the comparison of the classifier’s results and the classification results of the Canvas software (Figure .3).

Methodology There are 21 different classes for classification in the 100 images,

where some of them are overlapped (Figure .4).

Results

Parts of the Goals of R2CR2C, is discrimination of objects submerged in a multi-layered complex media, such as coral reefs in shallow waters. The image classification algorithm based on texture features could help us to extract the object of interest whose contrast has been reduced by the media from the background. We will apply this approach to the monitoring of Coral reefs and other underwater habitats (S4S4).

Relevance to the CenSSIS Strategic Research Plan

R1

R2Fundamental

ScienceFundamentalScience

ValidatingTestBEDs

L1L1

L2L2

L3L3

R3

S1 S4 S5S3S2Bio-Med Enviro -Civil