Assumptions implicit in remote sensing data acquisition and analysis
- 1 October 1990
- journal article
- research article
- Published by Taylor & Francis in International Journal of Remote Sensing
- Vol. 11 (10), 1669-1694
- https://doi.org/10.1080/01431169008955124
Abstract
The literature contains many examples of image acquisition and analysis which have been inappropriately applied and which have led to empirical results which may not be reproducible, or which are not conclusive. In this paper, we deal with eleven major assumptions which are implicit in the acquisition and in the analysis of passively sensed digital image data. It is hoped that an enumeration of such assumptions might lead to improved rules for image acquisition and analysis.This publication has 30 references indexed in Scilit:
- Calibration comparison for the Lands at 4 and 5 multispectral scanners and thematic mappersApplied Optics, 1989
- An update on visible and near infrared calibration of satellite instrumentsRemote Sensing of Environment, 1988
- The application of spatial filtering methods to urban feature analysis using digital image dataInternational Journal of Remote Sensing, 1988
- Selecting the spatial resolution of satellite sensors required for global monitoring of land transformationsInternational Journal of Remote Sensing, 1988
- A digital calibration method for synthetic aperture radar systemsIEEE Transactions on Geoscience and Remote Sensing, 1988
- Radiometric calibration of satellite sensors in the visible and near infrared: History and outlookRemote Sensing of Environment, 1987
- Calibration of NOAA-7 AVHRR, GOES-5, and GOES-6 VISSR/VAS solar channelsRemote Sensing of Environment, 1987
- Calibration of satellite radiometers and the comparison of vegetation indicesRemote Sensing of Environment, 1987
- Modeling the directional reflectance from complete homogeneous vegetation canopies with various leaf-orientation distributionsJournal of the Optical Society of America A, 1984
- Effect of instrument spectral response on use of the Landsat MSS for vegetative disease assessmentApplied Optics, 1980