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• W2189195112 abstract "In order to achieve a higher efficiency with remote sensing-based monitoring approaches, classification instructions have to be comparable to allow a transfer to other regions and fractions of time. It is shown that on the basis of the standardisation of the radiometric, spectral, geometrical and temporal information of remote sensing data a standardised classification was developed, which fulfil these requirements. For twelve typical agricultural crop types spectral standard curves were produced and a hierarchically structured algorithm was designed. This procedure, targeted on the identification of crops on the field level, takes up the differentiation between the phenology of the crops, illustrated in the standard curves, and converts it into a hierarchical classification strategy. The algorithm covers three basics processing units. In the pre-processing the available Landsat data of a cultivation year are standardized, masked and joined to a multi-temporal NDVI dataset of the agricultural lots. To obtain the classification instruction the parameters describing the crops must be extracted from the standard curves for the acquisition dates. The hierarchical classification covers four pixelbased image analyses using the Parallelepiped method and a final majority analysis of the lots. The determining advantage of the procedure consists in the way of building the classification instruction. While this working step in conventional procedures requires the largest time extent, the expenditure in the presented approach reduces to fitting the points of recording time in into the standard year of phonological development of each culture. According to this adjustment the culture specific spectral description can be extract from the standard curve directly. A quality evaluation on the basis of confusion matrices for 1620ha cultivated area on 144 parcels for the cultivation year 1995 resulted in an overall accuracy of 65,7%. In comparison to it a conventional Maximum Likelihood classification of the same database reached a result of 72,8%. That shows that the approach without quality loss is justified in principle. However the time-saving design of classification instruction and the easily repeated application of the procedure are the essential advantages. Spectral standard curves in their present level of development can be regarded as basis of the method. Their quality will continuously increase by steady adding of new information. They can be regarded as continuously expandable information memory and should take up all classification-relevant information to the appropriate culture in standardized form. This will lead to a quality increase with the application of the procedure. The perfection of the standard curves is thus a central starting point for the advancement of the presented procedure. By the use of the NDVI this method is expandable on other, also new sensor systems." @default.
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• W2189195112 date "2006-01-01" @default.
• W2189195112 modified "2023-09-27" @default.
• W2189195112 title "CROP CLASSIFICATION BASED ON SPECTRAL STANDARD CURVES" @default.
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