9 Results and Discussion of Processing Methods and Classifications Involving IKONOS Data
9.2 Results of Spatial Integration
9.2.2 Effects of Spatial Integration on Classification Accuracy
In the following, the effects of several spatial integration techniques on the classification accuracy are compared for 14-class maximum likelihood classifications. The overall agreement between classified data and the reference sample is just 40.5 % when the four multispectral IKONOS channels are used as input for the classification without any kind of spatial integration. The resulting map is very noisy (figure 29), manifesting the need for some kind of spatial integration.
Figure 29: Classification of the 4 m multispectral data without any spatial integration. For the legend see plate 44, appendix 2.
Spatial aggregation in square windows (block averaging) and post-classification mode filtering When the spatial resolution of the four multispectral IKONOS channels is reduced from 4 m to 8 m, 12 m and 16 m through block averaging, the classification accuracy is enhanced compared to the results for the 4 m resolution image (table 16). However, the accuracy values for the unfiltered classification results of the block averaged images do not differ significantly at the 95 % confidence interval, which is around ± 4.1 % for the overall accuracy values in tables 16 and 17.
When the results are mode filtered, classification accuracies are significantly increased. The effects of mode filtering exceed the effects of block averaging. 5×5 or 7×7 pixels are indicated as the optimal sizes for post classification mode filters. Even when the resolution is already reduced to 16 m, mode filtering still enhances classification accuracies significantly. The single highest overall accuracy value of 58.2 % is achieved when using the 8 m resolution data in combination with 5×5
post-classification mode filtering, but the accuracies achieved with a 7×7 mode filter or 4 m resolution data and the same mode filters are very similar (no significant differences).
Table 16: IKONOS ms channels 1-4, eastern test area, 14 class classification, overall accuracy [%], (overall Kappa index of agreement in brackets).
Post-classification mode filter
Resolution No filter 3×3 5×5 7×7
4 m 40.5 (0.342) 50.4 (0.447) 56.3 (0.512) 56.9 (0.520) 8 m 44.1 (0.379) 54.9 (0.495) 58.2 (0.531) 57.0 (0.516) 12 m 45.7 (0.397) 52.8 (0.471) 54.3 (0.488) 53.5 (0.479) 16 m 44.9 (0.386) 50.3 (0.442) 53.5 (0.478) 53.6 (0.479)
When the initial spatial resolution is already lowered to 8 m or less, 7×7 mode filtering suppresses a lot of spatial detail, producing maps which are not visually appealing. So even if the overall accuracy calculated from the reference sample is still high after 7×7 mode filtering the classification results of the block averaged data sets, the mode filter size in this case should be restricted to 3×3 or 5×5, which is enough to eliminate small misclassified areas. 7×7 mode filtering should only be applied to 4 m resolution data.
Although many of the single accuracy values in table 14 do not differ significantly from their neighbouring values (at the 95 % confidence level), the diagram in figure 27 points out the consistency in the accuracy values resulting from block averaging and mode filtering. There is a clear low point where the data are not spatially integrated at all and a clear peak at or close to 8 m resolution and a mode filter size between 5×5 and 7×7 (see also figure 31).
Optimal spatial resolution
The classification results show that, for the per-pixel multispectral classification of forest in this type of heterogeneous environment, the spatial resolution of 4 m is unnecessarily high. Assuming that no other kind of data integration takes place, the optimal resolution would be at least between 8 m and 12 m. This would be the spatial resolution to look for in data acquired for such a per-pixel multispectral classification. However, when one is already working with 4 m resolution data like IKONOS, one could argue that, as the classification accuracy using 8 m pixels is not significantly higher than for the 4 m pixels (especially if the results are mode filtered), the effort of pixel aggregation is not worthwhile and the original 4 m pixel size might be used as well, as long as the reduction of the file size through the reduction of the spatial resolution is not a desired effect.
As it was already indicated by the variograms and by the different rates of reduction of within-class variability with decreasing spatial resolution, the individual classes have different spatial characteristics, so that there are different ‘optimal resolutions’ for different classes. Figure 30 shows the products of class-specific user’s and producer’s accuracies for selected individual classes after classification and 5×5 mode filtering at different spatial resolutions. Among the examples, the class
‘dense pine forest’ seems to profit most from block averaging up to 16 m pixels, while 4 m seems to be the optimal resolution for matorral and broadleaved riparian forest. These class-specific accuracy values have to be interpreted with restraint, because the number of testing samples per class is only between 28 and 105 for these classes, leading to large and often overlapping confidence intervals. Even if different class-specific optimal resolutions were to be clearly established, it would not be practicable to conduct classifications with a separate resolution for each class, especially as the individual class areas are not known beforehand. For a general forest and land cover classification, a compromise spatial resolution has to be found, resulting in the best overall classification accuracy as described above.
0 0,1 0,2 0,3 0,4 0,5 0,6 PA*UA 0,7
BRF CF PFd Mat GL SFo
4 m 8 m 12 m 16 m
Figure 30: Product of user’s accuracy (UA) and producer’s accuracy (PA) for selected classes for IKONOS 4 channel 14 class classification (5×5 mode filtered).
Low pass filtering and post-classification mode filtering
Mean filtering enhances the classification results significantly and also significantly more than block averaging, where the best result without additional mode filtering was 45.8 % overall accuracy for 8 m resolution, compared to 57.3 % for the 5×5 mean filter (table 15). Post-classi-fication mode filtering leads to additional significant accuracy improvements in combination with the 3×3 mean filter. When the results of mean and median filtering are compared, the median filtered images look better (less blurred), but the classification results are not as good. The overall accuracy is 45.8 % for the 3×3 median filter and 52.7 % after additional 3×3 post-classification
mode filtering, compared to values of 51.6 % and 56.6 % when a mean filter is used. Accordingly, only the mean filtered channels were used as a basis for further classifications.
Table 17: Overall accuracies [%] for 14 class classifications of low pass filtered Ikonos ms channels 1-4, eastern test area (Kappa index of agreement in brackets).
Post-classification mode filter
Figure 31: Diagrams of overall accuracies for 4 channel multispectral classification (14 classes) with different spatial resolutions, pre-classification mean filters and post-classification mode filters used.
The 7×7 mean filter integrates the spectral response of an area of 28 m × 28 m, about the size of a Landsat multispectral pixel, creating an increased number of pixels with ‘mixed class signatures’.
This leads to locally low classification accuracies, especially along class borders, and the erroneous appearance of ‘intermediate classes’ in these areas (e.g. ‘matorral’ erroneously appearing in the map between riparian forest and pasture, or areas along the border between pine forest and riparian forest being classified as dense secondary forest). This effect is also visible, but less pronounced, in the classification of the 5×5 mean filtered image. Although the 7×7 mean filtering also leads to improved overall accuracies compared to the unfiltered image, there is an unnecessary amount of blurring and loss of information through averaging associated with this filter size. In contrast, particularly the 3×3 mean filtering in conjunction with post-classification mode filtering is a very
effective method to increase classification accuracies through the spatial integration of high resolution data. The maximum overall accuracy for 14 classes achieved with this method and the four multispectral IKONOS channels is 61.1 % (table 17, figure 31).
Segmentation
The results of the nearest neighbour classification of the segmented images are listed in table 18 for four different scale parameters. The classified segmented images were not mode filtered, because there are no isolated pixel values in an object-based classification. Segmentation followed by an object-based nearest neighbour classification does not lead to significantly higher overall accuracy values than simple block averaging without mode filter followed by MLC. This is probably due to the inferiority of the nearest neighbour classification technique.
The lower classification accuracy for the level 4 segmentation (scale parameter 30) indicates that too many of the large object primitives from this segmentation cover several target classes (mixed objects / undersegmentation). By contrast, the use of the scale parameter 12 results in an overseg-mented image with small homogeneous object primitives which do not integrate the elements of the heterogeneous forest classes. This is confirmed by the fact that after the classification of the level 1 (scale parameter 12) object primitives, the classification accuracy does increase when the result is mode filtered after all, indicating that the original spatial integration was not sufficient. Scale parameters 16 and 20 seem to be most appropriate for the creation of image object primitives for object-based classifications or simply for the production of meaningful boundaries within which the data can be spatially integrated (averaged).
Table 18: Overall accuracies [%] for four segmentation levels achieved with object-based nearest neighbour classifications, and in one case MLC.
Hierarchical segmentation level OA for Object-based StNN Per-pixel MLC 1 (scale parameter 12) 45.8
2 (scale parameter 16) 45.4
3 (scale parameter 20) 46.8 54.4
4 (scale parameter 30) 42.5
The object primitives resulting from the level 3 segmentation (scale parameter 20) were exported into the PCI Geomatica environment with their mean values. This resulted in a 4 m resolution multispectral data set where the pixels belonging to one object primitive had identical values. In spite of the reduced number of training pixels with different values and the connected problems for the estimation of class statistics for a parametric classifier, class signatures were calculated from
this data (based on the training areas also used for the other pixel classifications), and a per-pixel maximum likelihood classification was conducted. The result was a map where every per-pixel of the same image object primitive had to be assigned to the same class. The overall classification accuracy of this map was 54.4 %, which is significantly higher than for the object-based nearest neighbour classification. This overall accuracy is also significantly higher than the accuracies achieved with block averaging without mode filtering, but not higher than the accuracies achieved with mean filtering or block averaging in combination with mode filtering.
Recapitulating, when using only the four multispectral IKONOS channels in the classification of the eastern test area, the best results were achieved with a combination of 3×3 low pass (mean) filtering of the multispectral channels before classification and a 7×7 post-classification mode filtering. The classification accuracy achieved with this method was 61.1 % for 14 classes (64.2 % for 13 classes, figure 32). The reduction of the spatial resolution through block averaging to 8 m (or even up to 12 or 16 m) leads to increased classification accuracies, but filtering operations were more successful in significantly increasing classification accuracies. Segmentation (figure 33) does not seem to be a competitive method for spatial integration in this context. An attempt could be made to improve the results of the object-based classification by fine-tuning the object-based classification algorithm, but this would need a lot of additional effort.
Trying to differentiate between 14 classes using just the four IKONOS multispectral channels does not result in very high accuracy values in any case, but spatial integration methods do lead to significantly increased overall accuracies compared to the initial value of 40.5 % for the case without spatial integration.
Obviously, there can also be too much spatial integration (e.g. a combination of 7×7 mean filtering and 7×7 post-classification mode filtering), leading to mixed pixel effects and a loss of spatial detail. The loss of spatial detail through mode filtering becomes apparent upon a visual inspection of the classification results (maps) before is leads to reduced accuracy measures as calculated from the reference points.
9.3 Effects of Integrating Texture Features
Through the GLCM texture calculations based on the 1 m panchromatic IKONOS image, some of the spatial information contained in the channel with the highest available resolution was integrated with the 4 m resolution multispectral IKONOS data sets, with the and 8 m and 12 m block averaged data sets and with the 15 m resolution Landsat data.
Figure 32: Classification of the 3×3 mean filtered multispectral data set, results are 7×7 mode filtered. For the legend see plate 44, appendix 2.
Figure 33: Maximum likelihood classification of segmented multispectral data (scale parameter 20). For the legend see plate 44, appendix 2.
9.3.1 Class Separability with and without Texture Channels
Signature separability according to the Bhattacharyya distance (BD) as calculated from equation 20 can take values between 0 and 2, with 0 representing no separability and 2 representing perfect separability. As indications for class separability, BD values below 1 are regarded as representing very poor separability, values between 1 and 1.5 represent poor separability, values between 1.5 and 1.9 moderate separability and values over 1.9 good separability.
The different class signature separabilites between all pairs of classes, based on the multispectral data with and without the inclusion of three texture channels, are described and discussed in the following with the 8 m resolution data sets as an example.
As can be seen in table 19, class separabilities according to the Bhattacharyya distance between the class signatures generated from the four multispectral channels at 8 m resolution were very poor in many cases. Only 26 of 91 class pairs are well separable (BD above 1.9) and 11 had BD values below 1 (very poor separability). Most of the separability values between the forest classes were below 1.5 (poor separability). With separability values below 0.5, the separation of open pine forest and matorral, of dense and open secondary forest, and of broadleaved riparian forest and agroforestry appears especially unpromising. This corresponds to the overall accuracy values below 60 % as presented in chapter 9.2.2 (44.1 % for the unfiltered 8 m data).
The inclusion of three GLCM texture channels in the generation of the class signatures increases all the separability values by an average of 0.2 and reduces the number of class pairs with Bhattacharyya distances below 1 (very poor separability) from 11 to 3 (table 20). There are no more values below 0.5. The largest increases in signature separability are registered for the class pairs matorral and open pine forest, grassland and open pine forest, as well as grassland and matorral.
This demonstrates how spectrally similar classes (e.g. open pine forest where the spectral signature is dominated by the undergrowth and thus very similar to that of matorral or grassland) can become much better separable when texture parameters are included. Among the forest types, the texture parameters particularly increased the separability between dense pine forest and dense as well as open secondary forest, between broadleaved riparian and palm forest, between dense pine and broadleaved riparian forest and between dense and open pine forest.
The BD values between forest types are now mostly above 1.5, and they are all above 1 except for two cases: dense and open secondary forest, and broadleaved riparian and dense secondary forest.
Table 19: Signature separability (Bhattacharyya distance), using the four IKONOS multispectral bands at 8 m resolution. CF: cloud forest, PFd: dense pine forest, PFo: open pine forest, SFd: dense secondary forest, SFo: open secondary forest, PmF: palm dominated forest, BRF: broadleaved riparian forest, AF: agroforestry, Mat: matorral, Cal:
calimetal, GL: grassland, Cr: Crops, BG: bare ground. Very poor separabilities are indicated in italics.
CF PFd PFo SFd SFo PmF BRF AF Mat Cal GL Cr BG
PFd 1.441
PFo 1.745 1.193
SFd 1.400 0.594 1.115
SFo 1.410 1.027 1.397 0.493
PmF 1.602 1.907 1.913 1.494 1.638
BRF 1.426 1.219 1.331 0.569 1.163 1.001
AF 1.535 1.491 0.985 0.880 1.417 1.270 0.462
Mat 1.610 1.333 0.494 0.962 1.162 1.812 1.377 1.033
Cal 1.887 1.894 1.778 1.502 0.884 1.798 1.721 1.757 1.601
GL 1.943 1.839 0.866 1.602 1.619 1.955 1.750 1.511 0.604 1.732
Cr 1.998 1.993 1.422 1.962 1.974 1.993 1.882 1.574 1.529 1.980 1.012
BG 1.996 1.997 1.810 1.992 1.996 1.998 1.994 1.956 1.885 1.998 1.572 1.619
W 1.998 1.986 1.777 1.981 1.991 1.998 1.973 1.914 1.882 1.997 1.823 1.810 1.506
Table 20: Signature separability (Bhattacharyya distance), using the four IKONOS multispectral bands at 8 m resolution and GLCM standard deviation, contrast and entropy. Very poor separabilities are indicated in italics, Bhattacharrya distances increased by more than 0.4 compared to the distances without texture features are printed bold.
CF PFd PFo SFd SFo PmF BRF AF Mat Cal GL Cr BG
PFd 1.488
PFo 1.856 1.561
SFd 1.632 1.227 1.451
SFo 1.706 1.593 1.521 0.640
PmF 1.920 1.986 1.976 1.721 1.808
BRF 1.677 1.607 1.581 0.805 1.327 1.473
AF 1.728 1.696 1.340 1.099 1.522 1.693 0.710
Mat 1.818 1.708 1.365 1.409 1.624 1.935 1.611 1.329
Cal 1.950 1.953 1.829 1.761 1.427 1.952 1.849 1.842 1.785
GL 1.987 1.951 1.723 1.962 1.972 1.999 1.943 1.878 1.276 1.901 Cr 2.000 2.000 1.953 2.000 2.000 2.000 1.998 1.988 1.882 1.996 1.322
BG 1.999 1.999 1.899 1.997 1.998 2.000 1.997 1.975 1.951 1.999 1.822 1.894
W 2.000 1.998 1.894 1.996 1.998 1.998 1.990 1.969 1.973 1.999 1.972 1.986 1.817
The class signatures of open and dense secondary forest, which represent closely related informational classes and which exhibit the lowest separability (0.64) of all class pairs in the spectral-textural separability matrix, were merged. The separability matrix for the revised classification scheme of 13 classes is shown in table 21.
Agroforestry and broadleaved riparian forest still have very poor separability, too, but to merge them would result in a mixed informational class. The separability between broadleaved riparian and secondary forest is also critical, but it was decided not to merge these signatures in order not to prematurely preclude the precision of the classification. There is still the option to merge these classes after classification – if the primary aim is to improve the overall accuracy (reliability) of the map. The signature separability of 65 of the 78 class pairs in the 13 class classification scheme is moderate or good (above 1.5). Among the forest classes, the forest types that are most separable from the other types are palm dominated forest and cloud forest. The only class with a BD below 1.5 to palm dominated forest is broadleaved riparian forest and the only class with a BD below 1.5 to cloud forest is dense pine forest.
Table 21: Signature separability (Bhattacharyya distance), using the four IKONOS multispectral bands at 8 m resolution and GLCM standard deviation, contrast and entropy for 13 classes (after merging the class signatures of Sfo and SFd).
Very poor separabilities are indicated in italics.
CF PFd PFo SF PmF BRF AF Mat Cal GL Cr BG PFd 1.488
PFo 1.856 1.561
SF 1.660 1.403 1.433
PmF 1.920 1.986 1.976 1.753
BRF 1.677 1.607 1.581 0.996 1.473
AF 1.728 1.696 1.340 1.233 1.693 0.710
Mat 1.818 1.708 1.365 1.457 1.935 1.611 1.329
Cal 1.950 1.953 1.829 1.542 1.952 1.849 1.842 1.785
GL 1.987 1.951 1.723 1.959 1.999 1.943 1.878 1.276 1.901
Cr 2.000 2.000 1.953 1.999 2.000 1.998 1.988 1.882 1.996 1.322
BG 1.999 1.999 1.899 1.997 2.000 1.997 1.975 1.951 1.999 1.822 1.894
W 2.000 1.998 1.894 1.996 1.998 1.990 1.969 1.973 1.999 1.972 1.986 1.817
The increased signature separabilities after the inclusion of texture indicate that texture features can be expected to help improve classification results. However, it has to be kept in mind that the signature separabilites are only calculated for the training samples, which represent typical areas of their respective classes and mostly not areas close to class boundaries in the image. Additional edge effects introduced through the texture features which were calculated in 15 m × 15 m windows will
thus not affect the signature separability but might adversely affect the overall classification accuracy.
9.3.2 Classification Accuracy with Spectral and Textural Features