Image-object detectable in multiscale analysis on high-resolution remotely sensed imagery

Landscapes are complex systems composed of a large number of heterogeneous components as well as explicit homogeneous regions that have similar spectral character on high-resolution remote sensing imagery. The multiscale analysis method is considered an effective way to study the remotely sensed images of complex landscape systems. However, there remain some difficulties in identifying perfect image-objects that tally with the actual ground-object figures from their hierarchical presentation results. Therefore, to overcome the shortcomings in applications of multiresolution segmentation, some concepts and a four-step approach are introduced for homogeneous image-object detection. The spectral mean distance and standard deviation of neighbouring object candidates are used to distinguish between two adjacent candidates in one segmentation. The distinguishing value is used in composing the distinctive feature curve (DFC) with object candidate evolution in a multiresolution segmentation procedure. The scale order of pixels is built up by calculating a series of conditional relative extrema of each curve based on the class separability measure. This is helpful in determining the various optimal scales for diverse ground-objects in image segmentation and the potential meaningful image-objects fitting the intrinsic scale of the dominant landscape objects. Finally, the feasibility is analysed on the assumption that the homogeneous regions obey a Gaussian distribution. Satisfactory results were obtained in applications to high-resolution remote sensing imageries of anthropo-directed areas.