[Quantitative analysis of the polarized reflection factor of influence of oil film on water surface and their interaction].

In order to extract more reflection characteristic information of oil film on water surface by using remote sensing technology, the authors measured and analyzed the multi-angle hyperspectral polarized reflection information of oil film based on the traditional remote sensing researches. The authors used polarization as the indicator of quantitative study, adopted a three-factor-three-level orthogonal test to analyze the incident angle, oil film thickness, band, and the influence of oil film on water surface from their interaction between each other. The results of variance analysis of this orthogonal test show that: the three factors and their interaction between each other all have an influence on the polarization of oil film; the interaction between incident angle and thickness of oil slick, and the interaction between band and thickness of oil slick both show a significant impact on polarization.

[Analysis of influencing factors of snow hyperspectral polarized reflections].

Due to the need of snow monitoring and the impact of the global change on the snow, on the basis of the traditional research on snow, starting from the perspective of multi-angle polarized reflectance, we analyzed the influencing factors of snow from the incidence zenith angles, the detection zenith angles, the detection azimuth angles, polarized angles, the density of snow, the degree of pollution, and the background of the undersurface. It was found that these factors affected the spectral reflectance values of the snow, and the effect of some factors on the polarization hyperspectral reflectance observation is more evident than in the vertical observation. Among these influencing factors, the pollution of snow leads to an obvious change in the snow reflectance spectrum curve, while other factors have little effect on the shape of the snow reflectance spectrum curve and mainly impact the reflection ratio of the snow.

[Study on the main influencing factors of mixed-pixel spectral characteristics].

Hyperspectral remote sensing can improve the identification and classification of surface features through the spectrum comparing and matching to achieve classification and recognition. Because of the spatial resolution of the sensor as well as the difference in complexity and diversity on the ground, mixed pixels in the image are prevalent in remote sensing. The problem of subpixel unmixing is a prominent issue in the quantitative application of remote sensing.