A new change detection approach based on non-parametric density estimation and Markov random fields is proposed in
this paper. As the concrete form of gray statistical distribution of remote sensing images is often difficult to be known,
the non-parameter density estimation method does not need the specific forms in advance, and is especially suitable for
the estimation problem of small samples, so we adopt the non-parametric density estimation method to obtain the precise
estimation of the probability density of statistical distribution of differencing image in the paper, and then perform multitemporal
remote sensing image change detection combining with MRF(Markov random fields)model for spatial
smoothing. The final experimental results show that the proposed method is effective.
In this paper, a new method named BQCGW (block-based method of combining quantized colors and Gabor wavelet
features) is proposed for image retrieval. HSV color space, in which measured color differences are proportional to the
human perception of such differences, is quantized into 67 kinds of representative colors. We also propose the use of
Gabor wavelet features for texture analysis. Images in the database are divided into nine blocks before extracting color
and texture features. Experiment results show that our method is feasible and valid.