Abstract. Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.
The big differences of the texture and shapes in the same type and certain similarities among heterogeneous types result in the difficult classification of fabric defects. Compared with traditional global statistical method, we put up a new solution, which makes use of the fabric defect local region features to keep the defect property and defect classification by Support Vector Machines (SVM). Based on small-samples learning machine of SVM, we obtain a good performance of less computational load and high recognition rate.
Band selection algorithm is most important in data dimension reduction of hyperspectral image. There are many algorithms of band selection, but there are only few methods to do algorithm evaluation. A method is put forward in this paper to evaluate the band selection algorithm of hyperspectral image. The amount of information, brightness, image contrast and definition are defined as 4 indexes to measure deferent data fusion based on various band selection results. Based on the measurement, the evaluation of band selection algorithm is realized. In the paper, the evaluation method is used in the compare of 4 common band selection algorithms, the result of measurement is analyzed and the feasibility is verified.
Dynamics model and Electro-hydraulic proportional control model were established using Simulink and AMESim, Co-simulation was did based on the model. Control Performance of erecting system is Improved based on fuzzy PID control algorithm. Simulation results show that, Overshoot at level exchanging is small, the response is fast and the stability is improved based on fuzzy PID control algorithm.
Along with the development of hyperspectral remote sensing technology, band selection algorithm of hyperspectral image has become the research focus of hyperspectral application. A band selection algorithm based on noise evaluation of hyperspectral image and named as LMLSD is proposed in this paper. All the bands of hyperspectral image are ranked according to the SNR calculated based on local mean and local standard deviation of every band. Simulation results show that the performance of LMLSD is better than classical band selection algorithms of ABS and SDAA. The algorithm of LMLSD has validity and feasibility in practical application.
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