**5. Application**

#### **5.1. Data used**

The mangrove is a type of vegetation that grows in water or in mud. We tested the proposed method on a SAR image of E-SAR program (Figure 4), registered on C-band (wavelength 5.66 cm) and VV polarization with a resolution of 6m acquired on the mangrove coastal region of Cameroon.

**Figure 4.** Experimental image SAR image of the mangrove region

The image obtained from Mount Cameroun region is also used. The studied site is situated in the south west of Cameroon. The Mount Cameroon is a volcano in activity. The image used (Figure 5) is a SAR image, acquired by ERS-1 satellite in C band (λ = 5.66cm) with VV polarization and SLC (Single Look Complex) format. Its spatial resolution is about 25m and the side of a pixel is 12.5m. This image of 8000 columns and 8269 lines has been acquired on the 7th November 1998.

Due to the lack of a *priori* knowledge on these test sites, various litho-structural maps, formation plant maps including topographic maps and lithographs at 1: 200 000 of scale allowed us to identify the various themes.

**Figure 5.** Original image of the Mount Cameroun region

### **5.2. Histogram and thresholds detection**

214 Cartography – A Tool for Spatial Analysis

**4.4. Algorithm classification** 

(Cocquerez et al., 2001).

formation;

image.

**5. Application** 

**5.1. Data used** 

region of Cameroon.

2003).

variance. The algorithm of the method of deployment is widely presented and discussed

The final image is classified by the method of detection modes and valleys of the histogram.

2. detect ݊ local extrema of Table ܪሾ ሿ, where ݊ is the number of classes desired. The abscissas of the ݊ local extrema represent the nuclei or centers of different classes of

3. group pixels of the image according to the criterion of minimum distance to the various

4. assign one color to the pixels belonging to the same class and display the resulting

The details of the third point of this classification algorithm can be found in (Akono et al.,

The mangrove is a type of vegetation that grows in water or in mud. We tested the proposed method on a SAR image of E-SAR program (Figure 4), registered on C-band (wavelength 5.66 cm) and VV polarization with a resolution of 6m acquired on the mangrove coastal

The image obtained from Mount Cameroun region is also used. The studied site is situated in the south west of Cameroon. The Mount Cameroon is a volcano in activity. The image

The classification algorithm implemented is summarized by the following steps:

cluster centers. Each pixel is placed in the class whose center is closest;

1. construct the histogram of the image (Table ܪሾ ሿ);

**Figure 4.** Experimental image SAR image of the mangrove region

The histogram of original image of the mangrove region (Figure 4) is illustrated on figure 6. The previous histogram is approximated by the regression curve ݂ which is shown on Figure 7.

The previous approach, which has the advantage of being simple and fast, is well suited to images having forms regularly distributed.

The texture image is obtained from the parameter "Mean" in order 3. The calculation was performed on a window of size 7x7 around each pixel. The choice of this window size is justified by the fact that we get results like the original image with best representation of thematic classes. In addition, we found that the larger of window size was wide, there were more smoothing of the resulting image with absence of fine structure in the image.

The histogram of the texture image is shown in figure 8 and corresponding signature is shown on the figure 9. The irregularities observed on the histogram of texture images

(Figure 8) obtained from the texture index of Haralick introduced on the transformed histogram (Figure 9) many irregularities that can make the fault detection of local minima. This is the main reason that pushed us in this work to adopt a parallel approach to detect patterns of valleys.

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 217

**Figure 8.** Texture image histogram obtained by index parameter of Haralick "Mean" on the mangrove

Table 2 shows the modes and valleys that were used in classification thresholds. It was obtained on the basis of exploitation of the histogram and the signing of the texture image

For reference, the histogram of the original image and the texture image obtained on the basis of the radar image taken on the Mount Cameroon region are shown on figures 10 and

**Figure 9.** Corresponding signature of the texture image histogram

which can be seen representations in figures 8 and 9.

region

**Figure 6.** Filtered image histogram illustrating the presence of two classes of intensity in the image of the mangrove region.

**Figure 7.** Corresponding signature of the filtered image illustrating the appearance of a polynomial function approximation by the least squares sense.

patterns of valleys.

the mangrove region.

(Figure 8) obtained from the texture index of Haralick introduced on the transformed histogram (Figure 9) many irregularities that can make the fault detection of local minima. This is the main reason that pushed us in this work to adopt a parallel approach to detect

**Figure 6.** Filtered image histogram illustrating the presence of two classes of intensity in the image of

**Figure 7.** Corresponding signature of the filtered image illustrating the appearance of a polynomial

function approximation by the least squares sense.

**Figure 8.** Texture image histogram obtained by index parameter of Haralick "Mean" on the mangrove region

**Figure 9.** Corresponding signature of the texture image histogram

Table 2 shows the modes and valleys that were used in classification thresholds. It was obtained on the basis of exploitation of the histogram and the signing of the texture image which can be seen representations in figures 8 and 9.

For reference, the histogram of the original image and the texture image obtained on the basis of the radar image taken on the Mount Cameroon region are shown on figures 10 and 11. Signatures (Figures 12 and 13) corresponding to each of the histograms are also immediately followed. It is interesting to note that the texture image was obtained from the parameter "Mean" with a window of size 5x5. These parameters were got after several experimental tests.

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 219

keep the same scale in both cases representation. The transformed histogram (Figures 9 and 13) favors the detection of local extrema of peaks from the accentuation of the regression line

**Figure 11.** Signature of the filtered image illustrating the appearance of a polynomial function

**Figure 12.** Texture image histogram obtained by index parameter of Haralick "Mean" on the Mount

well represented.

approximation by the least squares sense.

Cameroon region


**Table 2.** Detection thresholds for classification and color coding of thematic classes: case of mangrove region

**Figure 10.** Filtered image histogram illustrating the presence of one main class of intensity in the image of Mount Cameroon region.

It can be seen visually that the histogram of the image filtered even not enough to make a good partition of the base image (Figures 6 and 10). This explains the poor performance of these filters in scenes that contain fine structures such as lineaments, which are generally not well preserved by these filters. Similarly, the histogram of the filtered image does not favor the detection of local extrema accurately. To remedy this shortcoming, a proposal method for modifying the histogram is implemented. It consists of a transformation of the representation of the histogram of the SAR image. For this, a histogram of the envelope curve passing through the ends of each peak is plotted (Figures 7 and 11) using the method of least squares regression. To keep up the properties of both representations, we plan to keep the same scale in both cases representation. The transformed histogram (Figures 9 and 13) favors the detection of local extrema of peaks from the accentuation of the regression line well represented.

218 Cartography – A Tool for Spatial Analysis

experimental tests.

of Mount Cameroon region.

Color codes

Color code s

region

11. Signatures (Figures 12 and 13) corresponding to each of the histograms are also immediately followed. It is interesting to note that the texture image was obtained from the parameter "Mean" with a window of size 5x5. These parameters were got after several

Modes 38 50 76 89 115 153 172

Valleys 45 57 83 96 121 137 179

**Table 2.** Detection thresholds for classification and color coding of thematic classes: case of mangrove

**Figure 10.** Filtered image histogram illustrating the presence of one main class of intensity in the image

It can be seen visually that the histogram of the image filtered even not enough to make a good partition of the base image (Figures 6 and 10). This explains the poor performance of these filters in scenes that contain fine structures such as lineaments, which are generally not well preserved by these filters. Similarly, the histogram of the filtered image does not favor the detection of local extrema accurately. To remedy this shortcoming, a proposal method for modifying the histogram is implemented. It consists of a transformation of the representation of the histogram of the SAR image. For this, a histogram of the envelope curve passing through the ends of each peak is plotted (Figures 7 and 11) using the method of least squares regression. To keep up the properties of both representations, we plan to

Thresholds / Color codes

**Figure 11.** Signature of the filtered image illustrating the appearance of a polynomial function approximation by the least squares sense.

**Figure 12.** Texture image histogram obtained by index parameter of Haralick "Mean" on the Mount Cameroon region

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 221

The final classified image presents the results of unsupervised classification obtained with different thresholds identified above (Table 2). This result is obtained on the basis of 14 cluster centers. Thus, the classified image traces an occupancy map of the study site highlighting 14 thematic classes whose characterization using data from field missions, old maps and the lithographic charts, aerial photographs allowed us to establish different classes of information. The exploitation of these data led to the realization of the space map shown in figure 14.

Detecting the number of class by this approach remains a major challenge. However, after several experiments, we found that we could not indefinitely increase the number of classes. Indeed, it proved that beyond a certain value (14 in this case) noted almost no more change the final result. Moreover, for low numbers of classes, there was more of a smoothing of the

**Figure 14.** Satellite map from the mangrove region of the estuary of Douala, Cameroon

The classified image obtained from the 14 threshold and observing the color code (Table 2) is shown in Figure 14. This classification method belongs to the family of unsupervised classification. Indeed, the spectral classes are first formed. These classes are based solely on digital information data. As a result, the classification algorithm presents below is used to

**5.3. Results of thematic maps** 

*5.3.1. Case of mangrove region* 

final result with a merging of small units in large.

**Figure 13.** Corresponding signature of the texture image histogram

The image texture enhances the visual interpretation. Indeed, it contains information on the spatial distribution of color variations. This is observed in the texture image by the presence of dark shades, clear, smooth and gray. The texture image obtained does not allow partitioning of the image into separate classes because its histogram (Figure 7 or 11) does not present specific modes and valleys. It is again transformed using the histogram. This second representation offers the advantage of facilitating the visualization of local maxima and minima. Each representation has them peculiarities and shortcomings; however, the combined use of two methods of representation facilitates the detection thresholds for classification (Tables 2 and 3).

Table 3 shows the modes and valleys that were used in classification thresholds. It was obtained following the same methodological approach followed with the image data of the mangrove area.


**Table 3.** Detection thresholds for classification and color coding of thematic classes: case of Mount Cameroon region

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 221

#### **5.3. Results of thematic maps**

#### *5.3.1. Case of mangrove region*

220 Cartography – A Tool for Spatial Analysis

classification (Tables 2 and 3).

mangrove area.

Color codes

Color codes

Cameroon region

**Figure 13.** Corresponding signature of the texture image histogram

The image texture enhances the visual interpretation. Indeed, it contains information on the spatial distribution of color variations. This is observed in the texture image by the presence of dark shades, clear, smooth and gray. The texture image obtained does not allow partitioning of the image into separate classes because its histogram (Figure 7 or 11) does not present specific modes and valleys. It is again transformed using the histogram. This second representation offers the advantage of facilitating the visualization of local maxima and minima. Each representation has them peculiarities and shortcomings; however, the combined use of two methods of representation facilitates the detection thresholds for

Table 3 shows the modes and valleys that were used in classification thresholds. It was obtained following the same methodological approach followed with the image data of the

Modes 43 87 99 131

Valleys 55 90 97 103

**Table 3.** Detection thresholds for classification and color coding of thematic classes: case of Mount

Thresholds / Color codes

The final classified image presents the results of unsupervised classification obtained with different thresholds identified above (Table 2). This result is obtained on the basis of 14 cluster centers. Thus, the classified image traces an occupancy map of the study site highlighting 14 thematic classes whose characterization using data from field missions, old maps and the lithographic charts, aerial photographs allowed us to establish different classes of information. The exploitation of these data led to the realization of the space map shown in figure 14.

Detecting the number of class by this approach remains a major challenge. However, after several experiments, we found that we could not indefinitely increase the number of classes. Indeed, it proved that beyond a certain value (14 in this case) noted almost no more change the final result. Moreover, for low numbers of classes, there was more of a smoothing of the final result with a merging of small units in large.

**Figure 14.** Satellite map from the mangrove region of the estuary of Douala, Cameroon

The classified image obtained from the 14 threshold and observing the color code (Table 2) is shown in Figure 14. This classification method belongs to the family of unsupervised classification. Indeed, the spectral classes are first formed. These classes are based solely on digital information data. As a result, the classification algorithm presents below is used to

determine statistical natural groups of data. We obtain quite detailed thematic classes. The 14 classes provide a map of land of the study site with good delineation of the different classes (Figure 14).

Contribution of SAR Radar Images for the Cartography: Case of Mangrove and Post Eruptive Regions 223

Thematic classes are categories of interest that the analyst tries to identify in the images, as different types of crops, forests or species of trees, different types of rocks or geological

The result obtained in this study presents a double advantage. The first advantage is at the level of accuracy in the identification of certain classes of information like the class sea. The second one is in the identification of certain thematic classes within certain classes of information and therefore a precise characterization can help in highlighting other information, as for example, different canopies in terms of vegetation class information.

In Figures 10 and 11, we have uni-modal aspect of the histogram of the image after filtering. This makes difficult any exploitation for segmentation. To overcome this drawback, it then

Figures 12 and 13 show them with now m-modal shape observed on the histogram of the image texture. After several experiments, we retained eight cluster centers summarized in

For the Mount Cameroon region, the same approach as that used previously on the mangrove is applied and the result is presented on Figure 15. The use of maps available and research work on this site (Akono et al., 2005, 2006) are used to characterize the eight thematic classes. The use of this information provides the classified image of the Mount Cameroon region including the specification of each class of information is summarized on the legend of Figure 15. As can be seen at the legend, we have nine more thematic classes instead of eight. This is because when classifying all pixels are not classified. All unclassified pixels were grouped in class vegetation. Furthermore, a broad thematic class (e.g. forest) may contain multiple spectral classes with spectral variations. Using the example of the forest, the spectral sub-classes can be caused by variations in age, species, tree density or simply the effects of shadowing or variations in illumination. The analyst's job is to determine the usefulness of different thematic classes and their correspondence to the

The purpose of this study was the production of space maps with the synthetic aperture radar (SAR) images. To achieve this, we proceed by adopting approaches optimized of texture analysis of images, using the statistical parameters of Haralick generalized at the order n. The approach is based on the concept of generic tree. It has the advantage of being less time consuming calculation from the conventional approach which frequently uses cooccurrence matrices for texture analysis and especially the processed images are generally very large sizes. In classification, the approach relies on the concept of detecting "modes" and "valleys" of histograms in a SAR image using classification of type unsupervised. For each of the SAR images, the histogram of the convolution image obtained at base of texture parameter is represented and approximated by a regression line called "signature" using

features, etc.

*5.3.2. Case of mount Cameroon region* 

table 3 with the color codes used for each class center.

uses to texture images.

thematic classes useful.

**6. Conclusion** 

**Figure 15.** Satellite map from the Mount Cameroon region

Thematic classes are categories of interest that the analyst tries to identify in the images, as different types of crops, forests or species of trees, different types of rocks or geological features, etc.

The result obtained in this study presents a double advantage. The first advantage is at the level of accuracy in the identification of certain classes of information like the class sea. The second one is in the identification of certain thematic classes within certain classes of information and therefore a precise characterization can help in highlighting other information, as for example, different canopies in terms of vegetation class information.

#### *5.3.2. Case of mount Cameroon region*

222 Cartography – A Tool for Spatial Analysis

**Figure 15.** Satellite map from the Mount Cameroon region

classes (Figure 14).

determine statistical natural groups of data. We obtain quite detailed thematic classes. The 14 classes provide a map of land of the study site with good delineation of the different

> In Figures 10 and 11, we have uni-modal aspect of the histogram of the image after filtering. This makes difficult any exploitation for segmentation. To overcome this drawback, it then uses to texture images.

> Figures 12 and 13 show them with now m-modal shape observed on the histogram of the image texture. After several experiments, we retained eight cluster centers summarized in table 3 with the color codes used for each class center.

> For the Mount Cameroon region, the same approach as that used previously on the mangrove is applied and the result is presented on Figure 15. The use of maps available and research work on this site (Akono et al., 2005, 2006) are used to characterize the eight thematic classes. The use of this information provides the classified image of the Mount Cameroon region including the specification of each class of information is summarized on the legend of Figure 15. As can be seen at the legend, we have nine more thematic classes instead of eight. This is because when classifying all pixels are not classified. All unclassified pixels were grouped in class vegetation. Furthermore, a broad thematic class (e.g. forest) may contain multiple spectral classes with spectral variations. Using the example of the forest, the spectral sub-classes can be caused by variations in age, species, tree density or simply the effects of shadowing or variations in illumination. The analyst's job is to determine the usefulness of different thematic classes and their correspondence to the thematic classes useful.
