中文摘要：

多层感知神经网络（MLP）是主流的非线性分解方法，但是目前缺乏有效方法处理MLP分解结果中的丰度负值问题。为此，提出一种可变神经网络结构的方法，逐步去除负值丰度对应的端元，并调整相应的网络结构使之针对剩余的端元进行分解。通过武汉地区模拟TM遥感影像实验可以发现，该方法与传统MLP方法以及线性光谱分解方法的平均误差分别为0．0777、0．08l9、0．0943，说明该方法的分解精度高于其他2种分解方法，能克服丰度负值问题。

英文摘要：

Spectral unmixing of remote sensing images is a hotspot in remote sensing field, and Multilayer Perception（MLP） neural network is a common nonlinear spectral unmixing algorithm. However, currently there is no effective way to deal with the negative abundances derived by the network. To solve this problem, a MLP neural network with variable architecture is proposed. By discarding endmembers with negative abundances, the MLP architecture is modified to unmix the rest endmembers, so a remote sensing image is finally unmixed. An experiment using a simulated TM image shows that the average errors of the proposed method, conventional MLP method and linear spectral unmixing model are 0.077 7, 0.081 9 and 0.094 3 respectively, thus the proposed method outperforms the other two. Therefore, the proposed method can overcome the negative abundance problem effectively.