Non-Parametric Classification of Data Sets Including DEM-Derived Data

In contrast to MLC, the non-parametric k-NN and ANN classifiers should not be adversely affected by the lack of normal distribution in some of the DEM-derived data channels. When the 8 m spectral-topographic data set is classified with non-parametric classifiers, however, the results are not more accurate than those achieved with MLC (table 27). With all three classifiers, the resulting accuracies for the spectral-topographic seven channel combination are either similar to the accuracies achieved with the spectral-textural seven channel combination, or lower (compare table 25). For some of the mode-filtered results, the overall accuracies are significantly lower at the 95 % confidence level. In other words, for the classification of the eastern test area, the spectral-textural channel combination is a better classification input than the spectral-topographic channel combination even when using non-parametric classifiers.

Table 27: Overall accuracy [%] and Kappa index of agreement (in brackets) for classifications of data set 27 (Ikonos ms channels 1-4, DEM-based elevation, slope and incidence60, at 8 m resolution) with different classifiers (13 classes).

MLC k-NN ANN

Unfiltered result 51.5 (0.447) 51.3 (0.442) 50.8 (0.443) Result 3 × 3 mode filtered 57.1 (0.507) 53.3 (0.464) 53.0 (0.467) Result 5 × 5 mode filtered 58.4 (0.522) 56.4 (0.499) 54.0 (0.476)

In all cases of spectral-topographic classification, and especially in the ANN classification, artefacts in the resulting maps point out that the influence of the topographic variables was too strong and/or the training samples were not fully representative of the ranges of elevations, slopes and incidence angles of the respective classes. This resulted in the creation of phenomena like an ‘agroforestry altitudinal belt’ in the ANN result, where areas of broadleaved riparian forest and even of grassland were misclassified as agroforestry because the pixel elevations (and in part also slope and incidence angles) matched those of the agroforestry training samples. In the ANN as well as in the k-NN classifications, broadleaved riparian forest at high elevations was classified as palm dominated forest. The positive effect that the cloud forest in the high mountains is classified more homogeneously is offset by the introduction of a false lower boundary of occurrence which leads to

cloud forest in the north-eastern corner of the image being misclassified as secondary forest. Again this is due to a lack of training samples representing the lower areas of cloud forest. This highlights the difficulty of ensuring that the training data of all classes are really representative of the full ranges of all channel values, even of ancillary data channels which are included in a classification.

k-NN and ANN classifications were also conducted with more than seven channels. In these classifications, DEM-derived data channels were used in addition to the four multispectral and three textural channels. The resulting data sets had up to ten channels.

Table 28: Overall accuracy [%] and Kappa index of agreement (in brackets) for non-parametric classifications of spectral-textural-topographic data sets.

Post-classification mode filter

Data Set Classifier No filter 3x3 5x5

k-NN 48.9 (0.411) 60.4 (0.542) 65.8 (0.602) No. 14 (IKONOS ms channels 1-4,

GLCM texture ENT, SD, CONT)

ANN 49.2 (0.428) 59.3 (0.539) 63.7 (0.588) k-NN 53.7 (0.468) 61.9 (0.560) 62.7 (0.569) No. 32 (IKONOS ms channels 1-4,

GLCM texture ENT, SD, CONT,

elevation) ANN 50.4 (0.439) 53.0 (0.466) 56.4 (0.505) k-NN 50.3 (0.426) 61.1 (0.547) 65.1 (0.593) No. 33 (IKONOS ms channels 1-4,

GLCM texture ENT, SD, CONT, slope)

ANN 49.9 (0.432) 58.1 (0.523) 63.4 (0.581) k-NN 49.1 (0.414) 60.4 (0.540) 63.9 (0.580) No. 34 (IKONOS ms channels 1-4,

GLCM texture ENT, SD, CONT,

incidence60) ANN 48.7 (0.421) 57.1 (0.515) 61.7 (0.564) k-NN 54.9 (0.480) 60.5 (0.544) 62.1 (0.561) No. 35 (IKONOS ms channels 1-4,

GLCM texture ENT, SD, CONT,

elevation, slope, incidence60) ANN 49.7 (0.429) 54.7 (0.483) 58.0 (0.519)

Table 28 shows that in most cases, the two non-parametric classifiers perform similarly with regard to the overall accuracy. For data sets including DEM-derived data, the overall accuracies resulting from ANN tend to be slightly lower than those resulting from k-NN. When data sets containing an elevation channel were classified, the disadvantage of ANN compared to k-NN was most pronounced. In ANN classifications, the elevation always became too dominant, producing altitudinal belts of classes in the map to an extent which does not exist in reality (figure 40).

On the whole, it can be said that the inclusion of the available ancillary data in the classification and the use of non-parametric classifiers did not manage to improve the classification results for the eastern test area significantly beyond what had already been achieved with MLC and IKONOS-derived data (using a combination of multispectral and textural data and spatial integration). No classification of the 8 m resolution data sets containing DEM-derived data achieved an overall

accuracy above 55.6 % without post-classification mode filtering. With 5×5 mode filtering, the best result was 67.2 % accuracy. These best results were achieved with MLC for data set 28, which contains elevation data, in spite of the fact that the elevation data is not normally distributed for most training classes. This demonstrates the robustness of MLC towards moderate violations of its underlying assumptions. The best results achieved with non-parametric classifiers were insignificantly lower at 54.9 % without post-classification filtering (k-NN classification of 10 channel data set 35) and 65.1 % for 5×5 mode filtered results (k-NN classification of data set 33, including slope data).

Figure 40: ANN classification result of data set 32 (IKONOS ms channels 1-4, GLCM Texture ENT, SD, CONT, elevation), demonstrating how this classifier confines classes like pine forest, palm dominated forest, cloud forest, secondary forest and agroforestry to certain ranges of elevations which they then dominate to an unrealistic extent. For the legend see plate 44, appendix 2.

The lack of significant improvements of classification results when DEM-derived data are used as additional input channels is partly due to the low resolution of the available DEM, leading to inaccuracies in the DEM-derived data channels. If a better DEM were available, it might still be worth experimenting with the inclusion of more DEM-derived variables in the classifications.

Successful inclusion of DEM-derived data would also require that the training samples are carefully chosen with regard to the ancillary data channels from the outset. To make sure that the training samples are representative not only of the spectral and textural characteristics of the classes, but also of their elevations and other topographic characteristics, would require the establishment of a larger number of separate training areas for each class.