Abstract
Spatial conception exists in remote sensing imagery as well as spectral information. It acts as more importance role in dominant landscape objects detection in high-resolution remote sensing imagery. Multiscale analysis is a new approach to meet the requirement of how to use spatial information in classification. Compared with traditional pixel based classification methods, multiscale analysis is composed of two fundamental components: the generation of a multiscale representation and information extraction. The paper focuses on one segmentation techniques- Fractal Net Evolution Approach (FNEA) and its usage in improvement in coastal remotely sensed image classification. FNEA is considered as one of effectual region-based segmentation and its threshold is a combination of size and homogeneity. We discuss two different segmental strategies which are speed-first and scale-first, and their impacts on image-objects. We can get the optimal segmental scale by analyzing the relationship between average size of each image-object and the different scale.
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