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According to the analysis and research of SFMEA and SFTA technology, this paper provides an FPGA software security test excitation random generation technology based on SFMEA and SFTA reverse synthesis. Firstly, establish the FPGA software fault tree through SFTA. Secondly, the SFMEA is performed for the important bottom event, analyze the potential fault effect and supplement the fault tree. Then refine the test constraints according to the bottom event. Finally generate the security random test stimulus using the verification language. This technology can effectively improve the sufficiency of FPGA software security testing, standardize the testing process, and ultimately improve software security and ensure software quality.

In this paper, the method of texture information extraction based on wavelet transform is discussed in detail, then the Principle Component Analysis(PCA) is performed to SPOT5 image, using software MATLAB, the texture information extraction of the first principle component basing on wavelet transform is completed, and two type of texture information are achieved, they are mean texture image and standard deviation texture image. Through analysis of the texture information and band combinations, the selected bands with texture information are used in classification, the method of Maximum Likelihood Classification is executed in the study, comparing to the applying the spectral information only, the overall accuracy raises from 67.3% to 81.9%, and the kappa index raises from 0.676 to 0.764 when using texture information. The result showed that classification overall accuracy and kappa index are all improved in the study.