Assessment of mapping accuracy of Landslides using image classification techniques

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Assessment of mapping accuracy of Landslides using image classification techniques

Image Classifications

Minimum Distance Classifier

In this classification, the existing landslide deposits (Figure 1) were considered as training area. In the other words, existing landslide deposits were used to compile a numerical " interpretation key" that described the digital values of Landslides in each input parameter image. Each pixel in the image was then compared numerically to the interpretation key and labeled with either landslide or non-landslide class. To do the analysis, the minimum distance classifier was employed to make this comparison between unknown pixels and the interpretation key pixels.

In this task, the authors first took six Landsat TM-images as input for minimum distance classification then the six terrain images were added to the TM's to create the second set of input images. This allowed an evaluation of the improvement of landslide classification over the initial TM-images.
Parallelepiped Classifier

In the parallelepiped classification, the ranges of values in each class may be defined by the lowest and highest pixel values in each image. In this study, the range of landslide class was defined by dividing the values (0 to 255) of TM images into 3,5, and 7 intervals, although other intervals could be chosen based on different algorithm and computer capacity. The otimal range of landslide class for each input parameter image and its mapping accuracy were obtained through computer search.
bayesian Classifier

Unlike equal weighting for input images in parallelepiped and minimum distance classifications, Bayesian classifier assumes that the importance of each input parameter image is unequal in terms of construction to an event (i.e. landslide event B). Bayesian theorem, introduced by Thomas Bayes in the 1800's, is a statistical approach concerning conditional, prior and posterior probabilities for inferential and decision-making procedures. It was applied in the study to calculate probabilities of Landslides.

In this study, the landslide deposits shown in the geological map were considered as the existing occurrences of Landslides for later prediction of the potential landslide area. The ratonale of applying Bayesian the Orem here was to revise the prior probabilities P (A₁) (i.e. existing Landslides) to posterior probabilities P (A₁|B) (i.e. predicted Landslides) through available information for predicting new or undetected Landslides.

Results and Discussion

Minimum Distance Classification

Twenty training areas consisting of the digitized landslide deposits were categorized as the potential landslide class; the only class defined in this study. Of those pixel values in images not lying in the range of potential Landslides class were classified to non-landslide class. Prior to executing the classification, the mean vectors of the six TM images and the six terrain images were computed in order to calculate the minimum distances (i.e. Euclidean distances) to class means for those input images.

Two sets of input images were chosen, although there could be thousands of combinations between those six TM images and six terrain images. One set of the input images analyzed was the TM images (dataset 1) , the other was all of the twelve TM and terrain images (dataset 2). Defining a proper Euclidean distance, ED_j , was the pre-classification task. Different ED_j resulted different classification with differing accuracy of prediction. Among the accuracy indices commonly used, mapping accuracy was applied to evaluate the accuracy of classification Mapping accuracy, MA, defined by short (1982) and Piper (1983), is usually applied to evaluate results for land cover classification. The advantage of applying this index is that MA possess the following characteristics equals zero if no positive match, equal one if perfect matches, takes into account user's accuracy, and producer's accuracy, and is not affected by sample size.

Figure 2 shows the results of minimum distance classification for six TM images alone and the six TM images and six terrain images combined. As noted in the diagram, the mapping accuracy of dataset 2 reaches its highest accuracy of 12.19% as EDj becomes 120. The highest mapping accuracy of dataset i was, however, much lower (i.e. MA = 3.65%) than that of the dataset 2. Figure 3 shows the classified image of dataset 2 with ED_j equals to 120, which was the optimal result for both of the input datasets.
Parallelepiped Classification

The main task for applying this classification was to define the ranges and logical operators between images. As a result of the classification, out put binary images showed the predicted landslide and non-landslide areas. The classification was performed empirically on the

basis of visual quality of the processed images and statistical characteristic of the training areas . The optimal combination, which possed the highest mapping accuracy among TM and the processed images, and terrain images was again obtained through computer search. The following is the resulting algorithm from the search. Figure 4 shows the output image from this equation with mapping accuracy of 9.25%

{ ( 28 Ł TM2 Ł37).AND.( 650 m Ł CONTOUR INTERVAL Ł 850m).AND . {DRAINAGE = 1250 m dilation AND (LITHOLOGY = coal-bearing)}

Bayesian Classification

Table 1 lists the range of pixel values of each of each input parameter image having the maximum weighting factor (I.E. (w⁺- w^-) The larger value of (w⁺- w^-) indicated the higher capability for distiguishing Landslides and non-landsliding areas. The DN_flt, which was dilated by 35 two hihest values of (W ⁺ -W). This meant that the fault image, DN_grp, were the two most important factors for istinguishing Landslides and non-landsliding areas in Healy, Alaska . The

(black: predicted landslides, white polygons: existing landslides)

Table 1. Summary of the optimal veighting factors for each input image

Pattern	W⁺	W^-	W⁺-W^-	Pattern	W⁺	W^-	W^-
TM1[60,68]	0.6511	-1780	0.8291	DN_slp [00,01]	0.2569	-0.1326	0.3895
TM[22,31]	0.5310	-0.3660	0.8970	DN_asp[68,82]	08026	-0.0318	0.8380
TM3[25,36]	0.5018	-0.2735	0.7753	DN_con[14,14]	1.3133	-0.4166	1.7300
TM4[37,50]	0.3345	-0.1918	0.5263	DN_flt[35,35]	1,4677	-3,4357	4.9034
TM5 [21,24]	0.0911	-0.0140	-0.1015	DN_dra[35,35]	0.5345	-1,9501	2.4846
TM7[19,19]	0.1550	-0.0031	0.1581	DN_grp[06,06]	0.7273	-4,1137	4,8410

pixel values of the images lying in the specified ranges listed in Table 1 were replaced by W+, otherwise by W- for each image, to form a weighted image of itself. Then, by applying Bayesian formula, which integrated posterior probability of each input parameter image, the posterior probability image was obtained. The pixel values of posterior probability image ranged from 0 to 1. The two datasets which had been used in parallelepiped classification were also chosen for creating posterior probability images.

Figure 5 shows the posterior probability image of dataset 2. The brighter area on the image indicates the higher probability to landslide. Based on the histograms of posterior probability images, various threshold values were chosen to creating binary images showing lanslide and non-lanslide classes. Figure 6 shows the variation of mapping accuracy vs. various threshold values selected for both sets of input data. The best result of Bayesian classification was obtained by processing both terrain and TM images with threshold value of 10-1 (Figure 7)

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