**Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions**

Janvier Fotsing, Emmanuel Tonye, Bernard Essimbi Zobo, Narcisse Talla Tankam and Jean-Paul Rudant

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48073

## **1. Introduction**

200 Cartography – A Tool for Spatial Analysis

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With the advent of new satellite sensors of type synthetic aperture radar (SAR) (ERS-1 and ERS-2, JERS-1, and RADARSAT), a large number of satellite images are currently available. However, radar remote sensing has a major drawback which is the difficulty to extract the information it contains. Since 1992, several works are conducted with the radar images on Cameroon to study the ecosystem of the coastal zone (Baltzer et al., 1996; Rudant et al., 1997) and the Mount Cameroon region (Akono et al., 2005, 2006; Talla, 2008).

Texture analysis is a robust approach of processing satellite radar images. It is a set of mathematical techniques to quantify the different gray levels present in an image in terms of intensity or roughness and distribution. Several methods of texture analysis exist and can be classified into two broad categories: structural methods and statistical methods (Haralick et al., 1979). Structural methods are used for describing the texture by defining primitives and "rules" of arrangement between them. Statistical methods are used for study the relations between a pixel and its neighborhood. They are defined according to different orders: 1, 2, 3, ...,n. The second order is the most classic, based on co-occurrence matrices. Obtaining these matrices is very time-consuming calculation, which has prompted researchers to not usually go beyond the second order in the evaluation of textural parameters and provides high order information to lower levels (Li, 1994). The matrices of order greater than two are called matrices of frequency. In texture analysis, the interest of researchers is moving increasingly towards optimization methods of evaluation time statistical parameters. Indeed, (Unser, 1995) replaced the co-occurrence matrix by the sum and histograms difference that define the main axes of the probabilities of second order stationary processes.

© 2012 Fotsing et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Fotsing et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

(Marceau et al., 1990) propose an approach for their textural and spectral classification of different themes and adopt a reduced level of quantification (16, 32 instead of 256). (Peckinpaugh, 1991) for his part describes an efficient approach for calculating texture measures based on co-occurrence matrix, thereby saving valuable time. (Kourgli et al., 1999) present a new algorithm to calculate the statistical parameters of texture through various histograms. Furthermore, Akono et al. (2003) have proposed a new approach in the evaluation of textural parameters of order 3. The present study is a generalization of the work of Akono et al. (2003). It proposes a generic tree method in the evaluation of textural parameters of order n≥ 2 near a window image which is to explore as if it were a tree, while memorizing the visited nodes.

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 203

Husoy, 1999; Reed & Hans Du Buf, 1993). For most of these methods, a single texture parameter is applied to the discrimination of classes. In this chapter, which applies to images from a radar sensor, we introduce the notions of vector texture, patterns and valleys of the histogram and textural signature for the characterization of classes of land, and show that textural parameters of order higher than 2 are more effective for discrimination of these

In the following, we will present the arborescent method of textural parameters evaluation, followed by the presentation of notion of mode and valley of histogram in SAR image analysis. The criteria of choosing textures parameters are also presented. Once the characterization of the various training zones is done and the classification algorithm is

The usefulness of image classification is not to be demonstrated today. A good classification requires a better identification of information classes on the image. This identification

Our methodology is divided into two parts: the first part concerns the improvement of the computational time required for the evaluation of textural parameters. The second part

Basically, statistical textural parameters are function of the occurrence frequency matrix (OFM), which is used to define the occurrence frequency of n-ordered gray levels in the

In an image with *Ng+1* levels of quantification, the OFM of order *n >1* is a *(Ng+1)n* size matrix. In this matrix, each element ܲభమڮ expresses the occurrence frequency of the *n-*ordered pixels *(i0,i1, …,in-1)* following the connection rule *Rn(d1,d2,…dn-1,θ1,θ2,…θn-1)*. This connection rule defines the spatial constraint that must be verified by the various pixels of the *n-*ordered pixels *(i0,i1,…,in-1)* used in the occurrence frequency matrix evaluation. This rule means that the pixel *ik+1 (0<k<n)* is separated to the pixel *ik* by *dk–1* pixels in the *θk* direction. For the sake of

A parameter of texture ܲܽݎܽ in any order ݊ is a real function defined in general manner by

presented. Finally, we present some experimental results.

requires the selection of good feature parameters.

deals with an approach of SAR images classification.

*3.1.1. The occurrence frequency matrix (OFM)* 

*3.1.2. The textural parameters* 

the equation given after:

**3.1. Formulation of high order of statistical textural parameters** 

simplicity, *Rn(d1,d2,…dn-1,θ1,θ2,…θn-1)* will be noted by *Rn* in the following.

classes.

**2. Problem context** 

**3. Methodology** 

image.

Several studies have been conducted in the field of classification by texture analysis. (Ulaby et al., 1986) used texture parameters from the method of co-occurrence to identify four classes of land cover in radar images. (Lucieer et al., 2005) propose a segmentation method based on texture parameters varied for multi object recognition on an image. The authors of this study include an operator called "Local Binary Model," modeling the texture, in a hierarchical segmentation to identify regions with homogeneous texture in an image. In (Linders, 2000), three methods (the method of fuzzy logic, regression analysis and principal component analysis) are used to select significant texture parameters for discrimination of different forest canopies. Recognition of forest cover is then performed by the method of neural networks. Puissant et al. (2005) (Puissant et al., 2005) examine the utility of the textural approach to improve the classification accuracy in an urban context. Texture analysis is compared to multispectral classification. In this study, textural parameters of Haralick (Haralick et al., 1973) of the third order are used. More, (Jukka & Aristide, 1998) have used first-order textural statistics in classifying land used in urban areas by means of Landsat TM and ERS-1. (Franklin & Peddle, 1989) used a mixture of spectral data, topographic (elevation, slope, aspect, curvature, relief) and statistical co-occurrence of the second order for the classification of SPOT images and radar in the boreal. Their work showed that the co-occurrence matrices of second order contain important textural information that improves the discrimination of classes with internal heterogeneity and structural forms. Homogeneous classes of soil are characterized adequately by spectral information alone, but the classes containing mixtures of vegetation types or structural information were characterized more accurately by using a mixture of texture and spectral data. Methods in the literature usually consist create neo-channels of the original image by calculating various parameters of the image texture. The neo-channels created are then combined with each other and the original image, for the production of a classified image. In another study, an analysis of different estimators for the characterization of classes of texture on SAR image is performed (Oliver, 1993). In this study, estimators under the maximum likelihood method are evaluated. Note that this method requires knowledge of the shape of the probability density data to be processed. In (Oliver, 1993), it is also considered a probabilistic description of the texture classes according to the law K and to the Weibull model, which are often applied to characterize classes in SAR images. Other extraction methods of textural parameters have been proposed in the literature (Randen & Husoy, 1999; Reed & Hans Du Buf, 1993). For most of these methods, a single texture parameter is applied to the discrimination of classes. In this chapter, which applies to images from a radar sensor, we introduce the notions of vector texture, patterns and valleys of the histogram and textural signature for the characterization of classes of land, and show that textural parameters of order higher than 2 are more effective for discrimination of these classes.

In the following, we will present the arborescent method of textural parameters evaluation, followed by the presentation of notion of mode and valley of histogram in SAR image analysis. The criteria of choosing textures parameters are also presented. Once the characterization of the various training zones is done and the classification algorithm is presented. Finally, we present some experimental results.
