关键词: 元胞自动机, BP神经网络, 森林类型分类, 像元值, Landsat 5-TM影像

Abstract: [Objective] The results of forest resources monitoring with remote sensing are often affected by the method of forest type classification, so this study proposed a forest type classification method based on cellular automata (CA), and the proposed method was tested with a Landsat-TM imagery, aiming at improve the classification accuracy, obtained results can be provide a technique support for remote sensing monitoring of forest resource.[Method] The study area was located in Dailing Forestry Management Bureau of Lesser Khingan Range, based on a good quality Landsat5-TM imagery covering the study area acquired in 2010 and forest inventory data of the study area in 2012, by using window method to obtain the 5^th band mean pixel value of all classes as center cluster, and the Moore model as the framework, the cell as the basic unit, the pixel mean value as the object, and the minimum distance method to obtain evolutionary rules, the judging criteria is that the distance between 8 cell which around the center cell and the each center cluster is the nearest as well as the number of pixels is largest which the center cell belongs to the this category. By compared to BP neural network classificationto illustrate the classification accuracy of the cellular automata.[Result] The forest type classification based on cellular automata resulted in an overall accuracy and Kappa coefficient of respectively 88.712 1% and 0.829 1, especially user accuracy of needle forest, broad-leaved forest and mixed forest respectively 73.60%, 92.94% and 94.13%, this results show that forest type identification is obtained the ideal result. BP neural network classification method achieved the overall accuracy of 86.671 3%, Kappa coefficient of 0.798 4.So the two classification methods can effectively identify the forest type information.[Conclusion] The cellular automata model used in remote sensing image forest type classification can make up the problem of the low classification accuracy caused by the low spatial resolution of TM imagery, it alsocan effectively improve the classification accuracy. And the cellular automata classification method can contribute to monitor the forest resource change and also provide an effective way to improve the efficiency of forest resources monitoring in large-scale areas.

Key words: cellular automata, BP neural network, forest type classification, pixel value, landsat5-TM imagery