Vegetation indices (VIs) are essential parameters widely used in the biosphere remote sensing retrieval, and the
relationship between the same vegetation indexes derived from different sensors is critical to long-term monitoring of
land surface properties. In this paper, MSAVI data derived from visible and near-infrared data acquired by the ASTER
and SPOT4 sensors were compared over the same time periods and pixel size. The results showed that the two VIs play a
higher correlation in high data field. ASTER MSAVI is more sensitive to vegetation coverage information. SPOT
MSAVI overvalues the local vegetation reflection signals significantly. The linear relationship between vegetation
coverage and MSAVI requires field sampling data to complete correction.
There are many different advantages and disadvantages in traditional subpixel classification methods such as uncertain classification accuracy, etc. which bring limitations for commonly application. In recent years, many algorithms have been used to resolve these problems. In this paper, based on an optimized image fusion algorithm, a comparison experiment on traditional maximum likelihood classification and neural net classification is performed. According to the classification accuracy data, the overall accuracy of classification increased from 81.67% to 89.67%.
An optimized point matching algorithm is introduced in this paper: the main idea is to extract ground control point by a new feature corner extraction method, then to search the sub-image unequdistantly with dynamic template during template matching calculation. The result of the experiment demonstrates that, the algorithm can extract valuable feature corners, it has more matching accuracy and efficiency, and it has more adaptability and applicable value.
A method of measuring the complex dielectric constant of the medium is proposed in this paper. Unlike the usualmethod, this method is based on measurement techniques and the numerical calculation. The simplicity and accuracyof this method is shown.