To meet the demand of large-scale agricultural monitoring system with remote sensing, extracting crop area planted must
be rapid, precise and reliable. In this paper, winter wheat identification with MODIS data in 2004 is taken as example in
North China. Applying spectral analysis and integrating genetic algorithm with neural network (GA-BP) is proposed,
which gives attention to two optimization algorithm, genetic algorithm and back propagation algorithm. According to the
spectral and biological characteristics of winter wheat, Red, Blue, NIR, ESWIR, LSWI, EVI are selected as characteristic
parameters. Then GA-BP algorithm is used for winter wheat identification. Results show that compared with maximum
likelihood and back propagation neural network classification algorithm, the GA-BP algorithm can not only run with
better efficiency, but also achieve best accuracy of identification. Therefore, it is the operational method for agricultural
condition monitoring with remote sensing and information service system at national level.
In this paper an approach based on particle filtering to extract complex object contour from imagery is presented. To deal with sharp tips of complex object, we take an adaptive state transition model with adaptive change in contour direction and adaptive number of particles. We not only use the information of luminance gradient but also use the speed and the direct of the move to compute the likelihood. Experimental results show that the proposed approach is capable of locating the target object contour of sharp tips accurately in the interactive way.
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.
Kinds of historical vector graphs have been gradually accumulated by ground truth data or other reliable sources, but these data have not been fully adopted to detect change in remote sensing circle. In this paper we describe a novel change detection method. The key feature of the new method is the use of a piece of historical land using vector graph. By combing one satellite image and the vector graph after necessary geometric rectification, we could detect change region of the satellite image corresponding to patches in the vector graph. Through adopting coefficient of part change and coefficient of entire change, the study calculates statistics indexes of image corresponding to patches of vector graph with different coefficient groups and assesses the computing results by kappa matrix. According to analytical results, the coefficient of entire change is more important to the number of commission error than the coefficient of part change. This method is benefit to the reuse of historical vector graphs. As the image-processing work of this method is based on patches of historical vector graph, it helps to the development of different vector graphs.