As the spatial resolution of remote sensing imagery has continued to improve

object-based image analysis (OBIA) methods have become widely used. The basic units of OBIA are segments produced by image segmentation rather than single pixels. Image segmentation is a prerequisite for OBIA and determines the quality of the analysis outcome. Image segmentation

a process of partitioning images into homogenous regions

is an indispensable step in analyzing remote sensing imagery. Typically

segmentation processes for high-resolution imagery are conducted via initial segmentation and primitive merging

and various segmentation methods have been developed. However

the quality of segmentation

particularly that of high-resolution remote sensing imagery

is less than satisfactory. Among numerous image segmentation methods

the seed-based region growing (SRG) method is relatively easy

efficient

and robust. This method depends largely on initial seed extraction

thus

the seed pixels should be representative and as similar to neighbors as possible. Different approaches have been proposed to select seeds

yet issues remain. For instance

redundant seeds are frequently chosen and consequently reduce the efficiency of SRG-based image segmentation. Also

details and thin linear objects cannot be accurately separated from large objects with inadequate seeds. With the goal of choosing representative seeds

an approach based on one-dimensional spectral difference (ODSD) is proposed to extract seeds for the SRG segmentation method

in which an evaluation criterion comprised of spectral angle and spectral distance is employed. The ODSD method is implemented as follows:1) the horizontal and vertical spectral difference maps of the image calculated; 2) the shallow basins in difference maps with "imHmin" function in Matlab are removed

and the local minima in each directional spectral difference image are obtained as candidate seeds; 3) overlaid candidate seeds are merged from orthogonal directions into one seed

which has minimum differences from adjacent neighbors; 4) remaining candidate seeds are optimized by choosing unique seeds in areas connected in a single dimension; and 5) all optimized common seeds and optimized directional seeds are used for the region-growing process. After the image is initially segmented using the SRG method with selected seeds

the resulting primitives are merged using the fractal net evolution method in the eCognition package. Experiments based on an IKONOS image demonstrated that the proposed ODSD method proposed could be efficient for delineating details and thin objects

which current segmentation approaches accomplish with more difficulty. Moreover

the quantitative evaluation showed that the outcome of the ODSD method reached nearly the same accuracy as the eCognition package and were superior to the kernel graph cuts method on the same scale. The ODSD method proved efficient in offering optimal seeds in the flat areas

details

and thin objects

thereby guaranteeing the representativeness of the chosen seeds. Moreover

the ODSD method yielded highly detailed segmentation maps.