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Spectrum Simulation of Multi-spectrum Image Using Field Measured Endmember Spectrum

Author: ZhangZuo
Tutor: DingJianLi
School: Xinjiang University
Course: Cartography and Geographic Information Systems
Keywords: Spectral response function ALOS AVNIR - 2 multi-spectral image Measured endmember spectrum Spectral scale conversion
CLC: P237
Type: Master's thesis
Year: 2010
Downloads: 101
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Abstract

The feature spectral characteristics of remote sensing applications, remote sensing technology and its applications is essentially the problem of imaging and inversion of the spectral characteristics of the surface features, valid to take advantage of the remote sensing image analysis and problem-solving, and must be various types of surface features spectroscopy features a comprehensive, in-depth understanding of its variation. However, based on field measured endmember spectral remote sensing applications, due to the different resolution of the measurement platform and instrument, resulting in the same spectroradiometer shape and reflectance values ??there is a big difference, the synonyms spectrum \frequent quantitative laboratory-measured data and field test point measurement the SPECTRAL data and remote sensing image pixel spectral information is difficult to associate spectral data did not make the play its due role in the application of remote sensing, remote sensing thematic information The Inversion difficult. Thus, the spectral conversion is necessary to study different measurement scales. This paper aims to remote sensing image pixel spectrum measured endmember spectral interpolation and remote sensing image spectrum simulation of two different technical point of view, to achieve the conversion between the spectral scale. Article is to the north Tarim Basin in Xinjiang Weigan River - Kuqa River Delta Oasis of the study area, select bare soil, vegetation, two types of surface features as an object of study, first by ALOS AVNIR-2 sensor spectral response function, the wild measured endmember spectral fitting for multispectral discrete spectrum, the by instance data analysis showed that the intends and the multi-spectral data AVNIR-2 pixel spectral data with good correlation, on this basis, using spectral linear algorithm to establish endmember spectral remote sensing image like spectral transformation model, to achieve a quantitative spectral remote sensing multispectral pixel scale measured endmember spectral scale conversion, laid some foundation for Remote Sensing Quantitative Analysis. Selection of optimal spectral interpolation method, a typical feature spectral features for the object, spectral remote sensing images (LANDSAT-TM5) linear interpolation, nearest neighbor interpolation, cubic polynomial interpolation, Lagrange interpolation, spline The interpolation ultimately determine the remote sensing image corresponding to the the endmember spectral data on field measurements optimal interpolation pixel spectral interpolation method. The use of remote sensing image spectrum simulation technology to the measured The spectrometer spectral data obtained for the data source, through the spectral response function of the sensor, and study the performance of the analog sensor spectral data using quantitative methods, quantitative response relationship to establish the remote sensing images and measured spectra. Complete the analog spectral data to the remote sensing image data from the measured spectra. Help establish a quantitative analysis of the media, and to provide the basis and foundation for the quantitative application of the spectral data between the ground measured reflectance data with remote sensors record the spectral energy. Spectrum conversion from measured endmember scale to multi-spectral pixel scale comparison under uniform standards for both data and application of quantitative analysis to provide a reference. Remote sensing image pixel spectrum to the measured endmember spectral interpolation and remote sensing image spectrum to simulate two different technical point of view, uniform standards for the two data provides a way to achieve the conversion between the two scales, with certain practical significance. Because of the limited level, there are many of this study is insufficient to be developed, in order to start a discussion, the attention of the academia colleagues in this field research, to provide some support for the effective use and development of quantitative remote sensing spectral data.

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