基于主分量和独立成分分析的多光谱目标检测

不同材料的物体具有不同的光谱特性, 基于这一原理, 可以利用多光谱图像数据对不同的目标进行检测。对于具有相似或相同外形特征( 颜色和形状) 的物体, 利用全色图像一般达不到检测与识别的目的; 利用传统的多光谱目标检测方法, 则因计算量较大, 识别精度低等, 达不到满意的效果。提出了一种基于主分量与独立成分分析相结合的多光谱目标检测的新方法。通过对多光谱图像数据进行主分量分析, 可以降低多光谱的维数, 去掉冗余成份, 保留其主要信息; 对降维后的数据再进行独立成分分析, 提取各种目标的光谱特性, 实现目标的检测与识别。将这两种方法有机的结合起来, 发挥各自的优点, 实现对多光谱图像目标快速的检测与识别。以真假树叶( 真树叶和塑料树叶) 为例, 验证了该方法的有效性和正确性。

Target Detection of Multi-spectral Image Based on PCA and ICA

Spectral features of various materials are different from each other,so the target can be detected among a group of ones with different materials based on the principle.Usually,the targets with the same features,such as color and shape, can not be detected in the panchromatic images;or the traditional methods can not give approving results because of the large scale of data and low precision of detection.So,this paper presents a new target detection method for multispectral images and hyperspectral images based on the Principal component analysis (PCA) and Independent component analysis(ICA).The data of multispectral images is firstly processed by PCA method in order to reduce the dimension of original multispectral images and take the redundancies out for getting the main components of images.The disposed data will be processed using the independent component analysis(ICA) algorithm,and then the target can be detected from the abstracted spectral features.This paper takes the advantages of PCA method and ICA method,and makes the target detection in multispectral and hyperspectral images quickly.At last,the validity of this method is verified by a test which detects two kinds of leaves(artificial and real),using the new method by combining the ICA and PCA.