Image fusion is of great importance to various remote sensing applications because many Earth observation satellites provide both high-resolution panchromatic (Pan) and lowresolution multispectral (MS) images. A number of fusion methods have been proposed, such as intensity-hue-saturation fusion and wavelet transform fusion methods. However, further studies are still necessary to improve the fusion performance for new types of remotely sensed images, such as IKONOS or QuickBird images. We propose an improved bilateral total variation filter method for fusing such MS and Pan images based on regularization. First, the constraints on the MS and Pan images are imposed based on the observation model. Then, the improved bilateral filter is used as an a priori model to constrain the high-resolution MS images. Finally, the steepest descent optimization algorithm is used to obtain the estimated MS images. Fusion simulations on spatially degraded IKONOS and QuickBird images, whose original MS images are available for reference, respectively, show that the proposed approach has better spatial quality while keeping the spectral information of the MS images. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).
Covariance features enabled efficient fusion of different type of image features have low dimensions and covariance-based object tracking has been proved robust, versatile for a modest computational cost. In this paper, a method combined Monte Carlo method and covariance features is proposed. Monte Carlo method is used to determine the scope of the search target at the region level. Covariance features are used to model the objects appearance at the object level. An improved object matching and occlusion handling strategies are given, which are followed by an appearance model update method. Experiments show our approach is robust and effective for tracking the object with irregular movement and partial occlusions.
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