Once the input data (household satellite images) has been translated into theLBP8,1represen-
tation, we will have feature vectors comprise of 256 feature (as described in Sections 6.2 of Chapter 6). A feature selection mechanism is thus applied. As before the aim was to reduce the overall number of dimensions so that only those features that serve as good discriminators between classes are retained. However, because integer valued population sizes are used in the context of the regression analysis , as opposed to categorical class labels as in the case of classification, an alternative feature selection strategy was required. Correlation-based Feature Subset selection (CFS) [64] was therefore adopted for this purpose. CFS feature selection uses a wrapper method to find a best heuristic for finding the best feature subset most appropriate to regression analysis (numeric prediction).
Once the feature selection process was completed the images were represented in terms of the identified subset of the features vectors. Some form of regression model generation can then be applied. For the evaluation presented later in this chapter the following regression analysis methods were considered: (i) Linear Regression (Linear Reg), (ii) Least Median squared Linear Regression (LMedS) [150], (iii) Isotonic Regression (IsoReg) [14] and (iv) Support Vector Machine for regression (SVMreg) [165, 168]. As in the case of the classification algorithms used in the foregoing chapters the implementations used were those available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning workbench [186].
7.3
Evaluation
Using the data sets presented previously in Chapter 3, a predictive model was generated with respect to each data set using theLBP8,1representation and the four regression model generators
Table 7.1: Comparison of the different regression analysis approaches in terms of prediction performance
Learning method Site A Site B
Coef MAE RMSE RT(s) Coef MAE RMSE RT(s)
LinearReg -0.080 2.167 2.570 0.70 0.274 1.981 2.407 0.51 LMedS -0.288 3.262 3.894 0.05 0.215 1.952 2.353 0.04 IsoReg -0.309 2.382 2.841 0.12 0.156 1.940 2.295 0.08 SV Mreg -0.279 3.367 3.970 0.02 0.308 1.778 2.056 0.01 LinearReg+CFS 0.084 2.145 2.550 0.01 0.400 1.727 2.093 0.01 LMedS+CFS 0.252 1.988 2.373 0.01 0.428 1.687 2.038 0.07 IsoReg+CFS -0.202 2.287 2.706 0.01 0.109 1.912 2.282 0.04 SV Mreg+CFS 0.307 1.957 2.330 0.01 0.587 0.143 1.802 0.01
used: (i) Linear regression (Linear Reg), (ii) Least Median Squared regression (LMedS), (iii) Isotonic Regression (IsoReg), (iv) Support Vector Machine for regression (SVMreg), (v) Linear Regression with CFS (Linear Reg + CFS), (vi) Linear Median Squared regression with CFS (LMedS + CFS), (vii) Isotonic Regression with CFS (IsoReg + CFS) and (viii) Support Vector Machine regression with CFS (SVMreg + CFS). For the evaluation Ten fold Cross-Validation (TCV) was again used throughout, and the following measures were recorded: (i) Correlation Coefficient (Coef), (ii) Mean Absolute Error (MAE), (iii) Root Mean Squared Error (RMSE), and (iv) Run Time (RT(s)).
The obtained results are presented in Table 7.1. From the table it can clearly be seen that the SVMreg +CFS prediction learning method produced the best performance with respect to the Site A and B data sets (Coe f =0.307 andCoe f =0.587 respectively). In contrast, the Linear Regression learner produced the worst performance with respect to the Site A data set (CS=−0.080), while IsoReg +CFS produced the worst performance with respect to the Site B data set (Coe f =1.09).
The bar graph given in Figure 7.2 gives a different perspective on theCoe f values recorded in Table 7.1. From the graph it can be observed that: (i) the maximumCoe f was produced using SVMreg + CFS learning method with respect to the Site B data, (ii) theCoe f values with respect to the Site B data were positive values (Coe f>0) in every cases, this means that the predictor and response variables were alike, and (iii) theCoe f values with respect to Site A in the cases whereCSF feature selection was not used were negative values (Coe f <0), this means that predictors and response variable were not alike. The Coe f can be used to approximate accuracyAcc=Coe f2as shown in Figure 7.3. In the figure the eight approaches are listed along the X-axis while the associated “approximate” accuracy value (apx accuracy) is given along the Y-axis. From the figure it can be seen that the SVMreg +CFS prediction learning method produced the best performance with respect to the Site B data (Acc=0.340), while the Linear Reg and Linear Reg + CFS produced the best performance with respect to the Site A data (Acc=0.094). IsoReg +CFS with respect to Site B data set produced the worst
Figure 7.2: The comparison ofCoe f results with respect to the different regression analysis approaches
Figure 7.3: The comparison of “approximate” accuracy usingCoe fwith respect to the different regression analysis approaches
In terms of runtime complexity, from Table 7.1 it can also be seen that: (i) the run times used for generating the prediction models when the CFS feature selection mechanism was applied (predictably) were less than when CSF was not applied, because CSF reduced the number of dimensions in the feature space making it less complex; and (ii) the maximum recorded run time was with respect to the Linear Reg model (for both sites). The Linear Reg prediction model, generated using theLPB8,1representation and the CFS strategy, for the Site
A and B data set were as shown in Equations 7.1 and 7.2, respectively.
Family Size={−(0.1085)(normalised)LBP8,142+ (0.0645)(normalised)LBP8,186 +(0.2017)(normalised)LBP8,1102−(0.2070)(normalised)LBP8,1106 −(0.3565)(normalised)LBP8,1110+ (0.0899)(normalised)LBP8,1118 +(0.1190)(normalised)LBP8,1150−(0.0453)(normalised)LBP8,1155 +(0.1881)(normalised)LBP8,1165−(0.1432)(normalised)LBP8,1171+0.4242}
(7.1)
Family Size={(0.2547)(normalised)LBP8,111+ (0.0836)(normalised)LBP8,143 +(0.1408)(normalised)LBP8,144−(0.3142)(normalised)LBP8,1107 −(0.3241)(normalisedLBP8,1163+ (0.4095)(normalised)LBP8,1173 +(0.1126)(normalised)LBP8,1211+ (0.2103)(normalised)LBP8,1219+0.3796}
(7.2)
Equation 7.1 is the regression equation model generated using the Site A data set. The equation comprises two parts: (i) the ten CFS identifiedLBP8,1bin numbers (42, 86, 102, 106,
110, 118, 150, 155, 165 and 171) and their coefficients, and (ii) a global constant of 0.4242. Equation 7.1 is the regression equation model generated using the Site B data set. Again the equation comprises two parts: (i) the eight CFS identifiedLBP8,1bin numbers (11, 43, 44, 107,
163, 173, 211 and 219) and their coefficients, and (ii) a global constant of 0.3796.
7.4
Discussion
The overall prediction performance presented in the previous section, Section 7.3, indicated that the proposed population estimation mining, using regression analysis based on LBPs, per- formed well with respect to the two satellite image datasets considered (Site A and Site B). From the experiments conducted the main findings may be summarised as follows:
1. The prediction models generated using the Site B data set were more effective than when using the Site A data. This was because, as noted previously, the satellite images from site B (wet season) were more distinctive thus making the consequent prediction model more accurate than for the Site A data (dry season).
7.5
Summary
The main idea presented in this chapter was to considered a population estimation mining from satellite images prediction model using regression analysis based on the properties of individual households that are embedded in satellite images, which can then be used to predict the family size of each household and provided an estimate of population size for a given area. From the reported evaluation it was noted that, when using the Site B data set, theLPB8,1representation, coupled with the CFS strategy and SVMreg produced the best overall results. This prediction model was therefore one of the models used with respect to the large scale study described in next chapter.
Chapter 8
A Unified Process for Large Scale
Population Estimation Mining Using
Satellite Imagery
8.1
Introduction
from Chapter 1 that the main objective of the research presented in this thesis is to identify data mining techniques for predicting the population size to be associated with individual house- holds identified from satellite imagery. The work presented so far has considered a number of techniques (graph-based, colour histogram based and texture based) coupled with classifica- tion and regression for predicting population size with respect to given households. The idea is of course that the techniques are applied in a much broader setting to estimate population size in a given region according to the individual households in that region. To achieve this the techniques described earlier need to be incorporated into a large scale population estimation mining process. The nature of this process is presented in this chapter.
A schematic of the proposed large scale population estimation mining process is given in Figure 8.1. From the figure it can be seen that the overall approach encompasses two parts: (i) prediction model generation and (ii) prediction for unseen data. The top half of Figure 8.1 is concerned with construction of the desired prediction model (the reader might like to compare this schematic with the generic schematic of the prediction process previously given in Figure 2.14 in Chapter 2). The second part of the overall process (bottom half of Figure 8.1) is con- cerned with prediction model usage. This process consists of five individual steps: (i) collation of a set of satellite images covering a prescribed area delimited by two pairs of geographic coordinates (latitudes and longitudes) describing opposing corner locations for the area of in- terest, (ii) segmentation of households within the satellite imagery, (iii) duplication household eliminated in situations where the same household appears in more than one image, (iv) fea- ture extraction using the same feature set as used for model generation, and (v) application of the generated prediction model to predict family size for each household which is then later
with respect to this chapter.
Figure 8.1: Schematic illustrating the proposed large scale population estimation mining pro- cess
The rest of this chapter is organised as follows. Steps one to five of the large scale popula- tion estimation mining process shown in Figure 8.1 are considered in Sections 8.2 to 8.6. More specifically satellite image collection is discussed in Section 8.2, segmentation in Sections 8.3, duplicate household detection and pruning in Section 8.4, household representation in Section 8.5 and prediction in Section 8.6. Section 8.7 then reports on the evaluation of the proposed large scale population estimation mining approach, followed by some discussion in Section 8.8. Finally, the main findings and some associated conclusions are presented in Section 8.9.