Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [THP-1 HIGH SPECTRAL RESOLUTION MEASUREMENT]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AIRBORNE IMAGING SPECTROMETER DATA ANALYSIS APPLIED TO AN AGRICULTURAL DATA SET. K. Staenz and D. G. Goodenough

Document type:: Multivolume work

Structure type:: Chapter

552 AIRBORNE IMAGING SPECTROMETER DATA ANALYSIS APPLIED TO AN AGRICULTURAL DATA SET K. Staenz and D.G. Goodenough Canada Centre for Remote Sensing 2464 Sheffield Road, Ottawa, Ontario, Canada K1A 0Y7 ABSTRACT This paper describes the ISDA (Imaging Spectrometer Data Analyzer) preprocessing steps necessary to bring the imaging spectrometer data acquired with the Programmable Multispectral Imager (PMI) over an agricultural test site near Zurich, Switzerland to an acceptable data quality level for information extraction purposes. Such preprocessing steps include data quality assess ment, correction of data in the spectral, as well as in the spatial domains, the proper radiometric alignment of the five PMI cameras, and relative and absolute normalization. Special emphasis was directed towards the viewing angle effects result ing from the large PMI scan angle range of 72.5 degrees. The removal of this effect is very important for information extraction purposes with respect to spectroscopic analysis and classifica tion of airborne imaging data. Preliminary work in the classification of imaging spectrometer data of agricultural targets (e.g. wheat, corn, potatoes, etc.) is presented. The two most likely methodologies for classification of such data, feature selection followed by classification and 'full' spectrum classification, will be discussed with special emphasis on data reduction using the band-moment analysis approach. Key Words: Imaging spectrometer, data quality assessment, radiometric camera alignment, data normalization, data reduction, band-moment analysis, feature selection, classification. INTRODUCTION Application of remote sensing technologies to agriculture has resulted in numerous investiga tions of the spectral behaviour of agricultural objects (Buechel et al., 1989; Horler et al., 1984; Brown et al., 1980). Most of the remotely sensed data acquisition to date has been conducted within well-defined, non-contiguous bands. There have also been efforts made to collect spectral data with spectroradiometers on the ground in order to generate a full spectrum of the agricultural object. Unfortunately, such ground- based measurements usually do not provide sufficient spatial coverage under similar illumination conditions. In order to combine the spatial as well as the spectral resolution and, therefore, to increase the potential of remote sensing substantially, a new generation of sensor systems, such as the airborne imaging spectrometer (Vane and Goetz, 1988), has been developed. These instruments, such as NASA's AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) or Moniteq ' s PMI ( Programmable Multispectral Imager), acquire data simultaneously in many narrow spectral bands (up to 288), usually covering the VNIR/SWIR (Visible and Near Infra-Red/ShortWave Infra-Red) region of the electromagnetic spectrum. The volume and complexity of imaging spectrometer data, however, require new approaches in image processing, especially in data handling, prepro cessing, and information extraction. These areas are addressed in the Imaging Spectrometer Data Analyzer (ISDA), a software package developed at the Canada Centre for Remote Sensing for an efficient analysis of imaging spectrometer data (Staenz and Goodenough, 1989). Discussed in this present study are the results of an analysis involving an agricultural data set with emphasis on the different ISDA preprocessing steps required to bring the imaging data to an acceptable quality. These steps are extremely important for information extraction purposes and the utilization of the full potential of imaging spectrometer data. Preliminary results of a classification approach are also presented. DATA ACQUISITION AND TEST SITE The data set was acquired with the PMI sensor over an agricultural test area, approximately 30 km NW of Zurich, Switzerland on July 28, 1986 by the German Aerospace Research Establishment (DLR). The PMI sensor characteristics are summarized in Table 1. This instrument works in a rake mode providing a total of 40 profiles over the wave length range from 430 to 805 nm in 288 contiguous, 2.6 nm wide, spectral bands sampled at 1.3 nm intervals (Hollinger and Gray, 1987). The over flight was conducted under nearly cloud-free conditions (horizontal visual range approximately 15 km) from an altitude of 500 m resulting in a swath width of approximately 730 m by a scan angle of 72.5 degrees as well as a ground resolution at nadir of 3.3 m across and 10.4 m along track. The aircraft heading is approximately 33.0 degrees azimuth and the solar illumination geometry is given by a zenith angle of 29.2 degrees and an azimuth of 194.3 degrees. Major crop types within the flat test site include wheat, barley, corn, and potatoes. Ground reference information, such as plant height and density, age (planting data), degree of fertiliza tion, etc., was collected for these different object types. In addition, black and white, as well as false colour, aerial photography was used in support of the ground data collection, for preprocessing of the imaging spectrometer data and for information extraction. PREPROCESSING Problems were encountered during the process of analyzing the 16-bit PMI data set with respect to the data quality of the sensor, calibration and the aircraft attitude variations (Borstad et al., 1985). In order to maximize the accuracy of information extraction, it is important to address these problems. Figure 1 summarizes the

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