LAI RETRIEVAL OF REED CANOPY USING NEURAL NETWORK METHODS
CHEN Jian 1，NI Shaoxiang 2，LI Yunmei 2
1.School of Remote Seinsing，Nanjing University of Information Science andTechnology，210044，Nanjing，China；2. College of Geographical Science，Nanjing Normal University，210097，Nanjing，China
LAI retrieval from large scale area quickly and accurately is an important research in remote sensing fields. In this paper，a model is presented to estimate LAI of reed canopy from Landsat-5 TM image data. Firstly，the model classifies the background of reed canopy into soil and water，then calculates and output a lookup table (LUT) by use of FCR model. Following it，LAI mapping was conducted based on the BP neural network model，which was trained using the data of actual measurement and LUT. The results indicate that the method has strong nonlinear fitting ability，and is able to increase the accuracy of LAI results through reducing the background influence from background spectrum.
陈健, 倪绍祥, 李云梅. 基于神经网络方法的芦苇叶面积指数遥感反演[J]. 国土资源遥感, 2008, 20(2): 62-67.
CHEN Jian, NI Shao-Xiang, LI Yun-Mei. LAI RETRIEVAL OF REED CANOPY USING NEURAL NETWORK METHODS. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(2): 62-67.
2008, Vol. 20 
