期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2005
卷号：XXXVI-2/W25
出版社：Copernicus Publications
摘要：Every year sugar beet diseases cause lower sugar beet yields and qualities compared to the average. For that reason, high resolution field and airborne hyperspectral data is used to recognize a fungal sugar beet disease in a study area of south Germany. For the airborne part of the study, multitemporal hyperspectral remote sensing data is provided by an airborne Spectroradiometer (AVIS), which is operated by the Ground Truth Center Oberbayern (gtco, Germany). Additionally, tractor based multitemporal hyperspectral reflection data provided by the GVIS specrometer is used to validate the AVIS data and to compare to two classification results. To indicate the difference between healthy and unhealthy plants a supervised knowledge-based classification approach is used. To detect the sugar beet disease Rhizoctonia solani, the reflection results can be elaborated with hyperspectral vegetation indices. Therefore, the two multitemporal datasets are analysed by calculating the OSAVI, which is one of these vegetation indices. Finally, the resulting images are classified into several vitality classes
