Abstract
A method to disintegrate digital remotely sensed image is discussed in this paper. By which the information in original remotely sensed data can be disintegrated point by point into three components--solar direct illuminance (SDI), sky-scattering illuminnce (SSI) and atmospheric path radiance( APR). Because it is the result of the interaction of the natural spectral components with ground features and the atmosphere, the three components images play a more powerful role than the normal original remotely sensed data in the quantification inversion research of ground radiation energy, atmospheric environment condition, and remote sensing image pattern recognition.
Key Word: Remote Sensing, Digital Image, Analytic
1. Introduction
1The concept of image disintegration is widely used in remotely sensed digital image processing such as: to disintegrate panchromatic image into different band image according to spectral wavelength, to disintegrate image into high and low frequency parts by filter, to disintegrate image into the sum of wave element by Fourier transform and wavelet analysis Wavelet, and the mixed pixel disintegration etc…They are all effective method in remotely sensed image sensed image into three respective component images representing solar illuminance, sky-scattering illuminance and atmospheric brightness by the support of GIS will be discussed in the following.
2. Interaction of Terrain, Natural Spectrum and Remotely Sensed Data
The quantification relationships between remotely sensed data and natural spectrum and composition of ground pixel, and the quantification relationships between remotely sensed value of different band component pixel and slope direction and gradient of ground surface of the pixel is the bases of disintegration of remotely sensed digital image.
2.1 The quantitive relationships between remotely sensed data and ground illuminance and pixel natural spectral illuminance.
The quantitative relationships are as following:
where, K
is the intrinsic ration in MSS system;
and
is the ground spectral reflectance and the atmospheric spectral transmissivity of the pixel respectively;
is the pixel's solar illuminance (PSIOP);
is sky-scattering illuminance (SSI);
is atmospheric brightness (AB);
and
are the corresponding remotely sensed component value of the pixel respectively.
2.2 The Quantitative Relationship Between the Pixel's Illuminances on the Slope and Respective Remotely Sensed Data
2.2.1 The Relationship between PSIOS and the respective Remotely Sensed Data
As shown in Figure 1, given a solar elevation, the solar point-blank luminous flux on the
pixel's slope aspect,
, is
times of the solar point-blank luminous flux on the horizontal projective aspect,
Fig 1 The solar direct luminous flux on the slope aspect and the horizontal projective aspect
(2)
Where,
and
are respectively the pixel's ground sloped angle, the solar
elevation, the slope aspect and the sun azimuth angle. Because there is relationship between
pixel's slope aspect area
and its horizontal projective aspect area
as following.
We have the pixel's slope aspect luminous as:
Where
is the pixel's horizontal projective aspect luminous.
And the pixel's slope aspect remotely sensed data value can be:
