CAJ | 학술논문

针对籽棉表层多类难检异性纤维，包括纸屑、白发、丙纶丝、化纤和地膜等5种白色物质，采用高光谱技术和最小噪声分离（minimum noise fraction, MNF）分析方法对含有异性纤维的籽棉图像进行研究。该文在400～1000 nm的光谱范围内采集高光谱图像，根据光谱曲线选取子区域，应用最小噪声分离分析方法降维、去噪。取MNF变换后的前4幅分量图像，通过视觉评价，选定最佳成分图像并融合中值滤波、灰度变化等图像处理的方法确定最佳分割图像，提取异性纤维。试验结果表明，对于以上5种异性纤维，该方法的识别率达到91.0%。该研究可为棉花异性纤维检测系统的开发提供参考。
침대자면표층다류난검이성섬유，포괄지설、백발、병륜사、화섬화지막등5충백색물질，채용고광보기술화최소조성분리（minimum noise fraction, MNF）분석방법대함유이성섬유적자면도상진행연구。해문재400～1000 nm적광보범위내채집고광보도상，근거광보곡선선취자구역，응용최소조성분리분석방법강유、거조。취MNF변환후적전4폭분량도상，통과시각평개，선정최가성분도상병융합중치려파、회도변화등도상처리적방법학정최가분할도상，제취이성섬유。시험결과표명，대우이상5충이성섬유，해방법적식별솔체도91.0%。해연구가위면화이성섬유검측계통적개발제공삼고。
In order to improve the recognition accuracy of seed cotton foreign fibers, the identification method in hyper-spectral images based on minimum noise fraction (MNF) was proposed and applied to feature extraction to reduce the dimension of multispectral images. This method can reduce the numbers of hyper-spectral data, and made the images noise reduce to the minimum and also reduce the computational requirements for subsequent processing. This paper selected white foreign fibers and cotton, which were in small discrimination, as the research object with 512 bands in the wavelength range of 400-1 000 nm. The spectral subset was selected according to the spectral curve, and then reducing dimension and denoising by using analysis method of MNF. The best component image was selected from the first four component images of MNF transformation by manual visual evaluation. The methods of image processing including median filtering, gray change method and so on were used to determine the best image segmentation and then extract the different fibers. Experimental results show that, for more than 5 kinds of different fibers, the recognition rate of the method reached up to 91.0%.