Spatial Integration of IKONOS Data

The 4 m resolution (16 m²) IKONOS pixels are often not large enough to integrate all the different components of a forest class, resulting in high within-class spectral variability. Three separate methods are tested to create image primitives which are more representative of their whole class, and to reduce the within-class variability of the data.

8.4.1 Spatial Aggregation in Square Windows

The first method to aggregate groups of pixels is non-overlapping averaging in square windows (block averaging): The image is divided into non-overlapping square windows of 2 by 2, 3 by 3 and 4 by 4 pixels forming the new image primitives. The mean of the DNs (Digital Numbers) of the pixels within each window is calculated and assigned to this primitive, which is subsequently treated as a coarser resolution pixel (using the PCI Geomatica IIIAVG algorithm). This leads to upscaled versions of the multispectral image with 8 m, 12 m and 16 m spatial resolution (figure 19).

This corresponds to pixel (image primitive) areas of 64 m², 144 m² and 256 m² respectively. Hay et al. (1997) showed that this is a simple method for approximating the spectral response of a coarser resolution image, although the aggregated data is not exactly equivalent to data acquired at a coarser resolution, because the square window averaging does not take into account the effects of the

modulation transfer function (MTF) of the sensor and the atmospheric effects, both of which cause an inclusion of some information from neighbouring surfaces.

Figure 19: A detail of the 4 m resolution multispectral IKONOS image (RGB 432, above) and the same area after averaging in square windows to 8 m resolution (centre) and 12 m resolution (below).

8.4.2 Low Pass Filtering

Low pass filtering is used to reduce the within-class variability while keeping the spatial resolution at 4 m. An average (mean) filter is employed to smooth the four multispectral IKONOS image channels using 3 × 3 and 5 × 5 filter windows (figure 20). By calculating the mean value for areas of 12 × 12 m or 20 × 20 m and assigning it to the central 4 × 4 m pixel, this pixel now carries

information about that whole area. This was only done for the multispectral channels and not for the texture channels because the latter, having been calculated in 15 × 15 m windows, do already contain information about the surrounding pixels. Alternatively, median filtering of the four multispectral channels in windows of 3 × 3 and 5 × 5 is performed to smooth the image while preserving the edges.

Figure 20: The IKONOS sub-image after low pass filtering (3x3 average filter).

8.4.3 Image Segmentation

Another method for pixel aggregation is image segmentation. This method takes into account the similarity between the pixels that are grouped together and aims at producing meaningful image object primitives which can represent image objects or parts of image objects. The software eCognition was used for the image segmentation, with its region merging technique based on local spectral homogeneity and a shape criterion favouring compact forms (Baatz et al. 2002, see also chapter 2.3).

The four multispectral IKONOS channels (4 m resolution) were used as input for the segmentation, and in some variations also the GLCM entropy texture parameter calculated from the panchromatic band and resampled to 4 m resolution.

User defined parameters like input channels, weights and the scale parameter were adapted through interactive experiments until the resulting segmentations were satisfactory according to a visual inspection. The aim was to arrive at segmentations with homogeneous object primitives, i.e. without

‘mixed objects’ incorporating areas belonging to different classes (undersegmentation). At the same time the object primitives should be large and compact enough to integrate the elements of any one forest class, avoiding oversegmentation in the sense of the creation of object primitives which contain only a subset of class elements, for example only pixels representing the shaded parts of tree crowns.

Both spectral heterogeneity (hspec) and shape heterogeneity (hshape) were used to define the degree of fitting f (overall heterogeneity criterion) for a proposed merge between two object primitives.

( )

spec w h

h w

f = ⋅ + 1− ⋅ (11)

The weight w for hspec (‘color criterion’) was finally set to 0.75, leaving a weight of 0.25 for the shape criterion. (Increasing the weight for the spectral heterogeneity criterion resulted in smaller segments on average.)

The shape criterion hshape is a combination of ‘smoothness’ hsmooth and ‘compactness’ hcmpct in the form

The smoothness value is calculated as



where n is the size of the object primitives OP1 and OP2 and the proposed Merge of the two, l is the length of the object perimeter and b the perimeter of the bounding box (drawn around the object parallel to the raster). The weight for smoothness wsmooth within the shape criterion was set to 0.9, leaving a weight of 0.1 for the compactness value which is calculated as



The heterogeneity in the feature space hspec is calculated as the sum of the standard deviations σ of the DNs in the channels used as input for the classification:

( ) ( )

The weights for the channels wc were set to 0.5 for the blue channel, 1 for the green, red and GLCM entropy channels and 1.5 for the NIR channel.

Different scale parameters were set to influence the threshold of the degree of fitting f requested for a merge, resulting in different sizes of the image object primitives (figure 21, table 11). A multi-resolution segmentation was conducted producing four segmentation levels using the scale parameters 12, 16, 20 and 30.

Table 11: Scale parameters and sizes of resulting image object primitives in the segmentation of the eastern test area.

Area of the resulting image object primitives Scale parameter

Average area Minimum area Maximum area

12 (level 1) 833 m² 16 m² 7 980 m²

16 (level 2) 1 478 m² 32 m² 9 810 m²

20 (level 3) 2 635 m² 80 m² 31 500 m²

30 (level 4) 6 171 m² 160 m² 35 800 m²

Figure 21: Multiresolution image segmentation with scale parameter 16 (above) and 20 (below).

After the segmentation, eCognition’s vectorisation functionality was used to produce polygons representing the shapes of the object primitives. The object primitives were used for classification within the eCognition environment and additionally, the objects including their shapes and their mean values in four multispectral and three textural channels were exported as .shp files and used to create raster layers within the PCI Geomatica environment, where the pixel values were averaged for the object primitives created in eCognition.