一种端元变化的神经网络混合像元分解方法

遥感图像中普遍存在着混合像元,对混合像元进行分解是遥感图像处理中的难点,在端元（Endm ember）个数不变的情况下,往往得到的分解结果精度不高。本文基于fuzzy ARTMAP神经网络,提出一种基于端元变化的神经网络混合像元分解模型。首先利用混合像元与纯净端元之间的光谱相似性,判断出混合像元包含的端元个数及类别,然后结合fuzzy ARTMAP神经网络进行分解。实验结果表明：本文提出的方法比传统的线性混合模型及fuzzy ARTMAP神经网络模型的精度要高,而且更加符合实际情况。

A Neural Network Method of Selective Endmember for Pixel Unmixing

Remote sensing images contain a lot of mixed image pixels,but it is difficult to classify these pixels.If the number of pixel's endmember is regarded as unchangeable,the traditional pixel unmixing algorithm cannot get a good result.In this paper we develop a new method of selective endmembers for pixel unmixing based on the fuzzy ARTMAP neural network,which firstly compares the pixel's spectral to the conference one and then gets the number of endmember.When it is taken into account,we use an ARTMAP neural network to extract subpixel information.Finally,the experimental results show that the selective endmember algorithm has been improved over conventional ANN algorithms and conventional linear algorithms.