Multi-temporal remote sensing image registration is the key step of change detection, and because of the remarkable
difference and the probably unknown of sensor parameters, the automatic registration of different temporal remote
sensing images is very difficult. Image registration based on Fourier-Mellin transform(FMT) is a global and phase
correlation method, which is based on Fourier and Log-polar transform. This method finds the transformation parameters
for registration of the images while working in the frequency-domain and it is resilient to noise, occlusions and so on. In
this paper, an improved approach based on Fourier-Mellin algorithm is proposed for the registration. Spectrum aliasing
and resampling interpolation will bring errors during Fourier-Mellin transform. To get a better registration result, we
have improved it by adding window function and filtering to reduce spectrum aliasing and increase the robustness.
In China, the contradiction of urban land use and cultivated land use is predominant, it's important to detect the urban
land use cover for the guide of urban development. The primary problem of dynamic detecting on urban land use cover
is how to get accurate classification of remote sensing data. Theoretically, if combining several low precision classifiers,
a better classification result can be made and this paper introduces how to combine the low precision urban land use
cover classifiers. We use CBERS (China-Brazil Earth Resources Satellite) remote sensing images of the year 2007 for
Shanghai's urban land use cover. We adopt the AdaBoost combination classifier, which combines spectral feature
information, texture structure information and improved Normalized Difference Built-up Index (NDBI) to improve the
individual classification precision. The experiment results show that a notable improvement of classification precision of
urban land use cover is achieved after using AdaBoost algorithm.
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