Multi-modal knowledge base generation from very high resolution satellite imagery for habitat mapping

Abstract

Monitoring of ecosystems entails the evaluation of contributing factors by the expert ecologist. The aim of this study is to examine to what extent the quantitative variables, calculated solely by the spectral and textural information of the space-borne image, may reproduce verified habitat maps. 555 spectral and texture attributes are extracted and calculated from the image. Results reached an overall accuracy of 65% per object, 76% per pixel, and 77% in reproducing the original objects with segmentation. Taking into consideration the large number of different habitats queried and the lack of any ancillary information the results suggest the discriminatory power of the finally selected attributes. Potential and limitations are discussed.

Scale Optimization in Topographic and Hydrographic Feature Mapping Using Fractal Analysis

“…Bobick and Bolles (1992) [26] argue that the information stored in a layer processed with FNEA is considered as fractal, in terms of holding the same degree of non-regularity at all scales or (inversely) self-similarity across every scale. Previous research findings suggest that when FNEA is applied with satellite images, a power-law equation of scale factor vs. mean object size is derived [14,27,28]:…”