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SEGMENTATION OPTIMIZATION FOR AERIAL IMAGES WITH SPATIAL 
CONSTRAINTS 
Ruedi Boesch, Zuyuan Wang 
Swiss Federal Institute for Forest Snow and Landscape Research 
8903 Birmensdorf, Switzerland - boesch@wsl.ch, zuyuan.wang@wsl.ch 
KEY WORDS: Image Segmentation, Optimization Methods, Parameter Estimation 
ABSTRACT: 
Unsupervised segmentation methods are important to extract boundary features from large forest vegetation databases. Finding 
optimized segmentation algorithms for images with natural vegetation is crucial because of the computational load and the required 
reproducibility of results. In this paper, we present an approach how to automatically select optimized parameter values for JSEG 
segmentation. The parameter evaluation is based on a spatial comparison between segmented regions and manually acquired ground 
truth. City block distance will be used as error metric to define discrepancies between available ground truth and segmentation. 
Varying the parameter range of values systematically allows to compute corresponding error areas. The smallest error area represents 
the optimized parameter value.Dependent on the lightness distribution of the selected images and the chosen color quantization, the 
spatial comparison with the ground truth is limited to local optimization. 
1. INTRODUCTION 
High resolution images acquired by aircraft or satellites are an 
important data source for extracting landscape boundaries and 
other vegetation structures. Large scale landscape and forest 
inventories depend on reproducible methods for the delineation 
of distinct vegetation types. Unsupervised segmentation 
methods represent suitable approaches to extract the required 
delineation features. Compared to natural images in illustrations 
and other man-made contexts, remote sensing data for 
landscape inventories contains often vegetation textures with 
weak edge structures and faint background. 
Swiss National Forest Inventory (NFI) has to record state and 
changes of the Swiss forest and makes use of a large image 
database with 7000 aerial images, taken at a scale of 1:30000 
with a resolution 0.5m for interpretation and area-based analysis. 
The image acquisition took place over the last 5 years, therefore 
varying illumination and inconsistent color distributions for 
vegetation areas are inherent. 
2. SEGMENTATION METHODS 
Image segmentation techniques can be classified according to 
several schemes (Gauch & Hsia, 1992), (Cheng et al., 2001). 
They distinguish between statistical methods, edge detection 
techniques, split and merge algorithm, methods based on 
reflectance models and human color perception. The large 
number of segmentation publications over the last decades is 
impressive, but selection of the best method is still a difficult 
task (Espindola et al., 2006),(Kato et al., 2001). When a suitable 
method has been evaluated, the segmentation has to be 
parametrized carefully. The ill-defined nature of the 
segmentation problem itself does not ease the parameter 
selection task. Parameter values of segmentation methods 
consist not only of discrete box-filter dimensions, but also of 
scaling ranges, e.g. for region growing and color quantization. 
Therefore evaluation of the optimal segmentation with the 
corresponding optimal parameter set is far from being a solved 
problem (Unnikrishnan et al., 2007),(Ndjiki-Nya et al., 2006). 
To reduce the evaluation complexity and to achieve robuster 
comparability this paper focus on parameter optimization. 
2.1. JSEG 
Compared to man-made structures, colors for natural landscape 
types like forest or agriculture fields contain typically increased 
luminance and hue variance, mainly caused by fractional 
fluctuation of surface structures and temporal effects. Due to 
soft or missing edge structures, local mean values for hue and 
luminance are fairly similar, therefore segmentation algorithms 
reveal increased over- and under-segmentation effects 
(Monteiro & Campilho, 2006). This strongly influences the 
segmentation results and emphasizes the importance of a 
systematic and quantitative parameter evaluation. 
The unsupervised segmentation algorithm JSEG (Deng & 
Manjunath, 2001),(Wang et al., 2004) has been selected mainly 
due to its robust performance for different vegetation images. In 
this paper we focus on the parameter optimization for JSEG to 
achieve an automatic delineation of homogenous vegetation 
areas (Wang & Boesch, 2007). 
Systematic parameter evaluation is often limited by time and 
operational constraints and specially for applications with a 
broad usage expert values are therefore to be handled with 
caution. Often expert values are just derived to deliver 
working results. Empirical values often hamper the tedious 
search for better settings. But for complex implementations like 
JSEG, it is also obvious, that no simplified underlying 
mathematical model like a convolution can be assumed. The 
peer group filtering of the color quantization, iterative region 
growing and seed determination of JSEG cannot be reduced to a 
functional description, therefore according to neural network 
concepts (Tyukin et al., 2007), optimization of parameter 
selection remains an approximation problem with infinite 
randomization of parameter values. The published parameters 
for JSEG lead to robust results for image collections with 
homogeneous background (e.g. Berkeley Segmentation 
Database (Martin et al., 2004)), but forest inventory images are 
often prone to over-segmentation.