Effects of preprocessing Landsat MSS data on derived featuresImportant to the use of multitemporal Landsat MSS data for earth resources monitoring, such as agricultural inventories, is the ability to minimize the effects of varying atmospheric and satellite viewing conditions, while extracting physically meaningful features from the data. In general, the approaches to the preprocessing problem have been derived from either physical or statistical models. This paper compares three proposed algorithms; XSTAR haze correction, Color Normalization, and Multiple Acquisition Mean Level Adjustment. These techniques represent physical, statistical, and hybrid physical-statistical models, respectively. The comparisons are made in the context of three feature extraction techniques; the Tasseled Cap, the Cate Color Cube. and Normalized Difference.
Document ID
19840030236
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Parris, T. M. (Environmental Research Inst. of Michigan Ann Arbor, MI, United States)
Cicone, R. C. (Michigan, Environmental Research Institute, Ann Arbor MI, United States)