Processing Of Multispectral Imagery Research Articles

Remote sensing of soils and vegetation: regional aspects

Professor K. Ya. Kondratyev made a significant contribution to soils and vegetation research using data from airspace observations. This paper is a brief review of the part of Kondratyev's versatile activity dealing with radiation transfer theory for the atmospheric correction of multispectral satellite images. New opportunity is outlined of remote sensing data applications developing the elaborated techniques of multispectral imagery processing. The applications are presented by classification procedures of natural objects in the processed images and by retrieval procedures of quantitative parameters of forest and other types of vegetation such as the amount of green phytomass together with discrimination of forest species (coniferous, deciduous, mixed) for each individual pixel. Some results are shown of the relevant applications for multispectral satellite images of medium and high spatial resolution.

Robust Multispectral Image Registration Using Mutual-Information Models

Image registration is a vital step in the processing of multispectral imagery. The accuracy to which imagery collected at multiple wavelengths can be aligned directly affects the resolution of the spectral end products. Automated registration of the multispectral imagery can often be unreliable, particularly between visible and infrared imagery, due to the significant differences in scene reflectance at different wavelengths. This is further complicated by the thermal features that exist at longer wavelengths. We develop new mathematical and computational models for robust image registration. In particular, we develop a frequency-domain model for the mutual-information surface around the optimal parameters and use it to develop a robust gradient ascent algorithm. For a robust performance, we require that the algorithm be initialized close to the optimal registration parameters. As a measure of how close we need to be, we propose the use of the correlation length and provide an efficient algorithm for estimating it. We measure the performance of the proposed algorithm over hundreds of random initializations to demonstrate its robustness on real data. We find that the algorithm should be expected to converge, as long as the registration parameters are initialized to be within the correlation-length distance from the optimum