Abstract. This paper presents a methodology to incorporate both hyperspectral properties and spatial coordinates of pixels in maximum likelihood classification. Indicator kriging of ground data is used to estimate, for each pixel, the prior probabilities of occurrence of classes which are then combined with spectral-based probabilities within a Bayesian framework. In the case study (mapping of in-stream habitats), accounting for spatial coordinates increases the overall producer's accuracy from 85.8% to 93.8%, while the Kappa statistic rises from 0.74 to 0.88. Best results are obtained using only indicator kriging-based probabilities, with a stunning overall accuracy of 97.2%. Significant improvements are observed for environmentally important units, such as pools (Kappa: 0.17 to 0.74) and eddy drop zones (Kappa: 0.65 to 0.87). The lack of benefit of using hyperspectral information in the present study can be explained by the dense network of ground observations and the high spatial continuity of field classification which might be spurious. Key words: Hyperspectral, streams, supervised classification, kriging During the last decade the development of computational resources has fostered the use of numerical methods to process the large bodies of data that are collected in the geosciences. A key feature of geoscience information is that each observation relates to a particular location in space. Knowledge of an attribute value, say a heavy metal concentration or reflectance value, is of little interest unless the location of the measurement is known and accounted for in the analysis.
The closest approach to this paper is by Goovaerts REF , where the prior probability of the i-th class, P i (s), is modeled by indicator kriging.
Geostatistical incorporation of spatial coordinates into supervised classification of hyperspectral data
Journal of Geographical Systems
Geography Computer Science