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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
via Fourier transform inversion. Because image R(x, y) is an 
analytic signal, R r (x, y) and R t (x, y) form a quadrature pair. The 
amplitude envelopes of R(x, jy) may be recovered via 
A(x,y) = ^R r 2 (x,y) + R 2 (x,y) (10) 
The amplitude response A(x, y) is regarded as the texture 
features in the research. 
The log Gabor filter bank is used to convolute with each band 
to derive texture response in each scale and orientation. Com 
bining the texture information in all orientations, multi-scale 
texture features of each band are obtained. 
2.3 Watershed Transform 
The watershed transform is a well-known powerful tool for 
automated image segmentation. Because its resulting bound 
aries form closed and connected regions, it becomes one of the 
best choices of the segmentation of remotely sensed image (e.g. 
Li et al., 1999; Hall et al., 2004; Chen et al., 2006; Li and Xiao, 
2007), which need recognise all of the objects in the image. The 
presented watershed algorithm is based on an immersion 
process analogy (Vincent and Soille, 1991), in which the 
flooding of the water in the image is efficiently simulated using 
a queue of pixels. 
Watershed transform often produce over-segmentation in 
situations of high gradient noise, quantity error and detailed 
texture. There are many solutions to the problem. A marker 
based solution is chosen in this research that basins are flooded 
from selected sources rather than minima. To integrate the edge 
and texture information of the landscape objects in image, 
watershed transform is implemented based on edge features, 
and the marker image is calculated from texture features. The 
texture features are segmented at first using a moving threshold 
algorithm developed from Hill et al. (2003). This algorithm 
calculates the mean and standard deviation of the texture 
features. Then several binary images are produced at reasonably 
spaced thresholds using the mean and standard deviation. For 
each binary image, the number of closed and connected regions 
greater than the given minimum size is calculated. The thres 
hold with the maximum number of connected regions is used as 
the output marker image. With this algorithm, no a priori 
knowledge is required about the number of regions. One only 
needs to give the size of the minimum region, which is always 
constant to most images. 
The edge features can be reconstructed with the marker image 
and then be segmented based on the watershed transform. The 
image has produced several scale texture features after the log 
Gabor bank filtering. Use different frequency of the texture 
features can produce segmentation results in different scale. 
Generally, low-frequency texture features produce large-scale 
segmentation results, whereas high-frequency texture features 
produce small-scale segmentation results. 
2.4 Accuracy Assessment 
The decision of the best segmentation results usually relies on 
the accuracy assessment. However, similar to the segmentation 
theory there is no established procedure for the assessment of 
its results. A general classification of assessment methods has 
been proposed by Zhang (1996), but only very few studies 
employ the assessment on remotely sensed image. Carleer et al. 
(2005) proposed a supervised method using discrepancy 
measures between a reference and the segmentation, which is 
employed in the research. 
The reference map is obtained interpreting from the original 
image by remote sensing experts. The reference polygons then 
are converted to raster. To compare the discrepancy, the seg 
mentation results are overlaid on the reference map. Observing 
from each segment region, the largest part in the reference map 
is regarded as right segmentation of this region, the others are 
regarded as mix-segmented pixels. Two discrepancy 
evaluations are calculated: percentage of Right-Segmented 
pixels in the whole image (RS), and ratio of Region Count in 
segmented image to reference map (RC). For an excellent 
segmentation, RS will be close to 100%, and RC will equal to 1. 
In the assessment method based on reference map, if only RS is 
used, RS will be increased with the fragmentation of the 
segmentation, even it will be equal to 100% when the region 
size equal to one pixel, which becomes a meaningless 
segmentation. So RC is also important indicator to restrict the 
segment regions close to the reference regions. 
3. DATA 
The data used throughout the research is a 512x512 pixel sub 
image of a QuickBird-2 scene acquired in November 21, 2004 
(Figure 2). Geographically, this area represents a portion of the 
highly fragmented agricultural landscape typical of the 
Jiangning region of Nanjing, China. QuickBird-2 provides 16bit 
multispectral data in the red, green, blue and near-infrared 
bands at 2.4m spatial resolution and an libit panchromatic 
band at 0.6m resolution. For producing high-resolution 
multispectral images, the multispectral bands are sharpened to 
0.6m resolution using panchromatic band based on Pansharp 
method proposed by Zhang (2002) at first. The false colour 
image for shown in Figure 2 is composed with infrared, red, and 
green band. In the false colour image, the red colour represents 
vegetation, the black colour indicates water, and the white 
colour represents roads. 
Figure 2. Original image overlaid reference polygons