Classification results based on Real SAR data

In the following, the classification results using the mosaic of TerraSAR-X images and TerraSAR-X sub-scene over Hamburg are presented. The classification results will be discussed in terms of 1) confusion matrices and 2) the stability of the estimated param- eters will be analyzed by computing the means and variances. It is worth noting that the confusion matrix presented in the case of unsupervised classifications shows only the recognized classes.

1. Mosaic of TerraSAR-X images

The mosaic of TerraSAR-X images depicted in Figure 5.5 shows different texture scenes. The texture parameters of this mosaic were extracted and later, classified. In the following the results are presented.

• MAP-GMRF results

The unsupervised and supervised classifications results using the estimated texture parameters provided by MAP-GMRF, are presented in Figures 5.16(a)-(b), respec- tively. According to the confusion matrix of the unsupervised classification summa- rized in Table 5.12, this method was able to well-recognize four classes (T1, T4, T5, and T7). However, in scene T2, the small houses were well-recognized and some buildings were separated in the upper part. These kinds of buildings can be found in scene T7. T7 was in fact recognized by the great majority. Scene T3 was confused with scene T9, which represented different kinds of vegetation. Scenes T4 and T6 had two different types of agricultural fields, but, the method placed all fields into one class. The confusion matrix presented in Table 5.12 explains the goodness of the extracted features. Scene T5 is separated, with 80 percent as a new class. Scenes T3,T6,T8 were unrecognized. The results for supervised classification show a lower quality compared with unsupervised classification, for example, image T1 was only recognized in 73 percent and some confusion with other classes was introduced. However, scenes T5 and T9 were better recognized, scene T5 was classified in 97 percent, and scene T9 in 66 percent. In addition scenes T3,T6, and T8 were also recognized.

• MAP-ABM results

Figures 5.16(c)-(d) show the unsupervised and supervised classifications using the estimated parameters provided by the MAP-ABM. The unsupervised classification shows lower quality compared to the unsupervised classification obtained using the estimated parameters of the MAP-GMRF method. The MAP-ABM method sep- arated scene T1, scene T5, and scene T6 well, but it also recognized scene T2, T3, and T9 with more than 30 percent. However, the supervised classification obtained using the MAP-ABM was superior. The confusion matrix, presented in Table 5.13 shows the results for classification. Eight textures were separated with more than 85 percent accuracy, and only scene T8 was classified with only 55 percent.

Comparing both methods, the results are similar that ones obtained with Brodatz mo- saic. The average accuracy in unsupervised classification shows the superiority of MAP- GMRF (76.96% vs 61.17%), while in the supervised case MAP-ABM (85.20%) outperforms MAP-GMRF (81.30%).

5.4. QUANTITATIVE EVALUATION OFSARINFORMATION EXTRACTION 99

(a) Unsupervised classification of TerraSAR-X mo- saic. Average accuracy 76.96

(b) Supervised classification of TerraSAR-X mosaic. Average accuracy 81.30

(c) Unsupervised classification of TerraSAR-X mo- saic. Average accuracy 61.17.

(d) Supervised classification of TerraSAR-X mosaic. Average accuracy 85.20.

Figure 5.16: TerraSAR-X mosaic classification based on the estimated texture parameters provided by (a,b) MAP-GMRF method and (c,d) MAP-ABM method. MAP-GMRF is su- perior in the case of unsupervised classification, while MAP-ABM provides better results in the case of supervised classification.

• Stability of the estimated parameters

The result of the TerraSAR-X mosaic classification can be explained using the mean and variances of the texture parameters as shown in Tables 5.14(a) and 5.14(b), ob- tained using the MAP-GMRF and MAP-ABM methods, respectively. In these ta- bles, the texture parameters are represented by their mean value. Figures 5.13(c)- (d) depict examples of the first texture parameter. The texture parameters of the MAP-ABM had much higher variance and were deviated around the mean value. Therefore, K-means classifier has more difficulties separating the classes.

100 5. DATA-DRIVEN EVALUATION OF SARINFORMATION EXTRACTION METHODS

Table 5.12: Confusion Matrices for TerraSAR-X textures obtained in the case of unsu- pervised and supervised classifications using estimated texture parameters provided by MAP-GMRF.

(a) Unsupervised classification.

Class: T1 T2 T4 T5 T7 Yellow 95.57 4.12 0.31 0.00 0.00 Magenta 0.35 29.12 0.43 0.00 70.10 Blue 4.22 93.02 2.77 0.00 0.00 Cyan 6.86 7.84 83.37 0.00 1.92 Sea Green 0.00 0.11 0.00 79.85 20.04 Red 2.97 10.16 86.86 0.00 0.00 Maroon 0.00 2.13 0.06 0.90 96.91 Green 95.21 0.00 4.78 0.00 0.00 Purple 17.20 76.08 1.43 0.00 5.28 Average accuracy 76.96 (b) Supervised classification. Class: T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 73.19 0.00 2.17 0.43 0.00 0.57 0.00 19.16 4.48 T2 0.00 81.14 5.76 0.84 1.39 0.04 9.94 0.01 0.88 T3 0.07 1.58 80.36 3.46 0.00 0.92 0.00 0.00 13.60 T4 0.44 1.78 0.70 90.64 0.22 0.27 0.09 2.92 2.94 T5 0.00 0.19 0.00 0.07 97.30 0.00 2.44 0.00 0.00 T6 0.39 1.56 1.67 3.59 0.00 90.68 0.00 0.53 1.58 T7 0.00 9.65 0.08 0.40 6.19 0.00 83.67 0.00 0.01 T8 23.28 0.00 0.09 6.29 0.00 0.87 0.00 68.66 0.81 T9 2.09 5.81 15.22 1.48 0.02 2.98 3.34 2.96 66.11 Average accuracy 81.30

Table 5.13: Confusion Matrices for TerraSAR-X textures obtained in the case of unsu- pervised and supervised classifications using estimated texture parameters provided by MAP-ABM.

(a) Unsupervised classification.

Class: T1 T2 T3 T5 T6 T9 Blue 88.63 0.49 3.58 0.26 1.18 5.87 Magenta 0.14 36.50 38.10 18.87 3.46 2.94 Sea Green 0.12 8.74 34.60 54.90 0.63 1.01 Red 5.81 30.74 7.84 0.75 48.33 6.53 Maroon 0.00 2.76 4.40 92.80 0.00 0.04 Cyan 0.11 1.35 6.25 3.09 88.62 0.58 Green 0.00 12.05 17.88 70.07 0.00 0.00 Purple 53.85 4.25 10.82 3.46 2.17 25.45 Yellow 43.34 3.37 18.12 0.51 2.78 31.88 Average accuracy 61.17 (b) Supervised classification. Class: T1 T2 T3 T4 T5 T6 T7 T8 T9 T1 85.80 2.64 0.20 0.93 0.00 0.53 0.01 3.15 6.75 T2 0.16 91.64 3.12 3.04 0.00 0.52 0.09 0.00 1.42 T3 0.00 6.98 91.30 0.18 0.01 0.01 1.02 0.20 0.28 T4 2.94 5.35 0.65 87.00 0.00 0.17 0.00 0.96 2.94 T5 0.00 0.03 1.87 0.00 89.81 0.00 8.30 0.00 0.00 T6 0.03 4.13 2.33 1.04 0.00 92.46 0.00 0.01 0.00 T7 0.00 0.39 3.74 0.03 7.42 0.00 88.42 0.00 0.00 T8 37.85 0.07 6.31 1.12 0.00 0.02 0.00 54.61 0.02 T9 3.83 6.39 2.72 1.08 0.00 0.01 0.15 0.03 85.80 Average accuracy 85.20

2. TerraSAR-X sub-scene over Hamburg

This study case refers to a sub-scene over Hamburg in Northern Germany with 2000× 2000pixels size (see Figure 5.6). Both methods for despeckling and information extraction were applied over this image. The goal is to recognize urban areas from the sub-scene and obtain a land use classification.

In order to evaluate the efficiency of the extracted features, an unsupervised classification was performed, using the K-means algorithm with 5 classes. The supervised classifica- tion was performed using the maximum likelihood method by selecting some regions of interest. The proposed classes were: water (blue), high buildings (cyan), small building (yellow), vegetation (green).

Figures 5.17 and 5.18 depict the classification results provided by both methods. Here, it should be noted that, according to the unsupervised classification results, both methods well-separated the high buildings class. The MAP-GMRF recognizes small-buildings and forest better than the MAP-ABM. The MAP-ABM well recognizes the water class. The supervised classification shows the superiority of the MAP-ABM because it recognizes well all the classes, for example, in the right upper part of the image there is a small lake, which was correctly classified as water between the vegetation class, which was also well-defined. However, in the MAP-GMRF supervised classification, the vegetation

5.4. QUANTITATIVE EVALUATION OFSARINFORMATION EXTRACTION 101

Table 5.14: Mean and variance values of texture parameters for each tile (T1-T9) in the case of TerraSAR-X mosaic using MAP-GMRF and MAP-ABM methods.

(a) MAP-GMRF θ1 θ2 θ3 θ4 θ5 θ6 θ7 θ8 θ9 θ10 Mean T1 0.12 0.09 0.07 0.07 0.07 0.00 0.03 -0.00 -0.00 0.04 T2 0.27 0.28 0.04 0.15 -0.05 -0.03 -0.04 -0.06 -0.03 -0.02 T3 0.23 0.20 0.09 0.13 -0.00 -0.02 -0.01 -0.04 -0.03 -0.04 T4 0.11 0.31 0.05 0.03 0.01 0.10 0.03 -0.08 -0.00 -0.05 T5 0.44 0.28 -0.14 0.10 -0.04 -0.03 -0.09 -0.04 0.01 0.00 T6 0.11 0.19 0.04 0.12 -0.02 0.08 -0.02 0.06 -0.02 -0.03 T7 0.33 0.28 0.01 0.14 -0.06 -0.05 -0.06 -0.04 -0.02 -0.00 T8 0.11 0.09 0.06 0.08 0.06 0.01 0.05 0.01 -0.00 0.02 T9 0.22 0.19 0.08 0.13 0.01 -0.02 -0.01 -0.04 -0.03 -0.04 Variance T1 0.00129 0.00048 0.00024 0.00031 0.00057 0.00070 0.00049 0.00082 0.00053 0.00075 T2 0.00068 0.00185 0.00202 0.00083 0.00064 0.00043 0.00031 0.00048 0.00052 0.00069 T3 0.00093 0.00106 0.00033 0.00031 0.00090 0.00050 0.00068 0.00045 0.00025 0.00033 T4 0.00454 0.00746 0.00073 0.00241 0.00060 0.00291 0.00071 0.00268 0.00142 0.00091 T5 0.00633 0.00426 0.00261 0.00127 0.00088 0.00052 0.00096 0.00102 0.00095 0.00056 T6 0.00112 0.00072 0.00057 0.00030 0.00011 0.00090 0.00036 0.00089 0.00057 0.00030 T7 0.00145 0.00148 0.00132 0.00090 0.00061 0.00056 0.00034 0.00055 0.00053 0.00055 T8 0.00092 0.00045 0.00014 0.00021 0.00096 0.00104 0.00067 0.00062 0.00054 0.00045 T9 0.00166 0.00196 0.00043 0.00037 0.00100 0.00043 0.00103 0.00060 0.00022 0.00052 (b) MAP-ABM θ1 θ2 θ3 θ4 θ5 θ6 θ7 θ8 θ9 θ10 Mean T1 0.47 0.27 0.14 0.17 0.12 0.13 0.16 0.17 0.17 0.22 T2 1.83 1.40 -0.65 0.15 -0.55 -0.24 -0.12 -0.10 0.09 0.23 T3 1.90 1.23 -0.47 0.28 -0.57 -0.36 -0.15 -0.11 0.13 0.15 T4 1.16 1.22 -0.45 -0.08 -0.27 0.27 0.01 -0.05 0.21 0.05 T5 1.85 1.15 -0.42 0.53 -0.27 -0.28 -0.32 -0.15 0.06 0.04 T6 1.05 0.75 -0.34 0.25 -0.31 0.40 -0.08 0.31 0.08 -0.05 T7 1.90 1.31 -0.50 0.41 -0.50 -0.30 -0.26 -0.12 0.02 0.20 T8 1.00 0.49 -0.01 0.10 -0.07 0.01 0.07 0.10 0.12 0.15 T9 1.20 0.50 -0.03 0.08 -0.13 -0.03 0.07 0.10 0.15 0.20 Variance T1 0.23 0.04 0.03 0.01 0.03 0.02 0.00 0.00 0.00 0.01 T2 0.11 0.14 0.06 0.05 0.04 0.03 0.01 0.03 0.01 0.01 T3 0.05 0.06 0.04 0.03 0.03 0.02 0.02 0.01 0.01 0.01 T4 0.20 0.25 0.08 0.02 0.05 0.05 0.02 0.05 0.02 0.01 T5 0.04 0.05 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 T6 0.13 0.04 0.05 0.01 0.02 0.03 0.00 0.02 0.00 0.02 T7 0.05 0.06 0.04 0.01 0.02 0.01 0.01 0.01 0.01 0.01 T8 0.45 0.17 0.07 0.02 0.07 0.04 0.02 0.01 0.01 0.01 T9 0.61 0.18 0.12 0.04 0.11 0.05 0.02 0.01 0.01 0.01

102 5. DATA-DRIVEN EVALUATION OF SARINFORMATION EXTRACTION METHODS

(a) Unsupervised classification of TerraSAR-X Hamburg scene using GMRF estimated parameters.

(b) Supervised classification of TerraSAR-X Hamburg scene using GMRF estimated parameters.

Figure 5.17: TerraSAR-X Hamburg classifications using the estimated parameter pro- vided by MAP-GMRF. (a) Unsupervised classification. (b) Supervised classification. The classes and their associated colors are: water (blue), high buildings (cyan), small build- ings (yellow), vegetation (green). Urban area, small and high buildings, are well recog- nized. There is confusion between river and forest.

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(a) Unsupervised classification of TerraSAR-X Hamburg scene using ABM estimated parameters.

(b) Supervised classification of TerraSAR-X Hamburg scene using ABM estimated parameters.

Figure 5.18: TerraSAR-X Hamburg classifications using the estimated parameter pro- vided by MAP-ABM. (a) Unsupervised classification. (b) Supervised classification. The classes and their associated colors are: water (blue), high buildings (cyan), small build- ings (yellow), vegetation (green). Urban area, small and high buildings, are well recog- nized as well as river and vegetation.

104 5. DATA-DRIVEN EVALUATION OF SARINFORMATION EXTRACTION METHODS