摘要
高光谱遥感技术能够借助丰富的地物图像和光谱信息,反映目标地物与背景地物间的细微差异,从而将其区分开来。目前的小目标探测算法多侧重于从图像处理方面着手,文章则从光谱维数据分析的角度出发,利用光谱分析中的奇异值检测方法探测小目标,首先对关注区域的地物像元光谱进行连续统去除和正交变换等预处理;然后将每个像元的光谱对该区域平均光谱进行光谱匹配求其相似性,并实现高光谱数据降维;而后通过光谱角匹配值的马氏距离进行奇异值检测,将马氏距离大于自适应阈值的像元判定为小目标。该方法不需要任何先验信息,实验结果表明该方法运算量较小,运算速度快,并有较好的小目标探测准确度。
In the present paper, a new method was discussed, which used outlier detection algorithms of spectral analytical technology to tell the small target from background. First, continuum removal and standard normal variate were employed to pretreat the AVIRIS remotely- sensed data. It could be regarded that continuum is the absorption of background, and the characteristic absorptions are superposed on the continuum. So the continuum removal is frequently applied to remotely-sensed hyperspectral data to eliminate the contribution of background absorption and separate the characteristic absorption of concerned objects from background. SNV corrects each spectrum by subtracting the mean and dividing by the standard deviation for that spectrum. After the pretreatment, spectral angle mapping was used to reduce the dimension of hyperspectral data. Since this mapping process calculated the similarity between the spectra of each pixel and the average spectrum, no prior information such as standard spectrum library is required. And then, Mahalanobis distances were calculated. As Mahalanobis distance shows the extent to which samples deviate from the total population, the points whose Mahalanobis distance are larger than adaptive threshold were regarded as small target. The adaptive threshold was determined by data mean value and maximum value. Applying the algorithm above to a set of AVIRIS remotely-sensed hyperspectral data which was free downloaded from the NASA official website, small target on the concerned area was picked out correctly. In addition, the above mentioned took about 1/8 time as much as the traditional Mahalanobis distance method without prior reducing dimension of hyperspectral data. Compared with traditional algorithm, no prior information is needed, and less calculating work and time is required. Still, it has got a satisfying accuracy.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2008年第8期1832-1836,共5页
Spectroscopy and Spectral Analysis
基金
航天科技创新基金项目
国家"863-703"专题项目资助
关键词
高光谱
小目标探测
奇异值检测
光谱角匹配
马氏距离
Hyperspectrum
Small target detection
Outlier detection
Spectral angle mapping
Mahalanobis distance
