摘要
以SPOT5图像为研究对象,试验了4种图像分割方案,采用基于最终测量精度准则的多指标评价和基于欧氏距离的相似度综合评价两种方法,对分割效果进行评价,其中多指标包括圆度(RO)、紧致度(CO)、形状指数(SI)、最小包络椭圆短半径(RE)、椭圆度(EF)、形状因子(P2A)、面积相对误差(RA)、周长相对误差(RP)、中心位置绝对位移(DC)9个指标,相似度采用面积(A)、周长(P)、RO、CO、SI、RE、EF、P2A等8个因子计算。结果表明,原始图像直接用于分割的效果远好于经直方图均衡化后的图像。在图像分割过程中,输入图层的权重很大程度上影响分割效果,根据各输入图层标准差设置权重的分割效果,略好于根据图层信息量设置权重的分割效果。采用图像分割的方法自动提取小班边界,经适当后处理后编制工作手图用于森林资源规划设计调查,不但大量节省野外小班勾绘工作时间、降低劳动强度、提高工作效率,而且大幅度地提高了小班勾绘的准确性,确保面积调查精度。
The present implement of remote sensing in practical forest inventory don＇t take advantage of imagery processing and analysis technology of remote sensing, and is inefficient. Aimed to the auto-delineation of the boundary of forest sub-compartment for actual application, an imagery segmentation method was studied. SPOT5 imagery was segmented by four protocols, evaluation with nine features separately based on ultimate measurement accuracy, and synthetical evaluation of similarity based on Euclidean distance （ED） were used to evaluate the segmentation, here the features were roundness （ RO）, compactness （ CO ）, shape index （ SI ）, radius of smallest enclosing ellipse （ RE ）, elliptic fit （EF） and form factor （ P2A）, relative error of area （ RA）, relative error of perimeter（RP） and displacement of the center of polygon（DC）, while area（A）, perimeter（P）, RO, CO, SI, RE, EF, P2A were used to calculating ED. The result indicated that segmentation on raw imagery was better than that on histogram equalization imagery, the weights of input layers would affect the output of segmentation, and it would get better result by segmentation with the weight based on the standard deviation than that based on the information content of input imagery layers. It is feasible that preparing draft map for forest resources inventory through boundary auto-delineation based on imagery segmentation, for it is not only efficient and low labor intensity, but also improve the division precision of boundary and area accuracy of forest sub-compartment.
出处
《林业科学研究》
CSCD
北大核心
2010年第1期53-58,共6页
Forest Research
基金
国家自然科学基金项目(30872023)
广西壮族自治区林业局科学基金(200246)
关键词
图像分割
SPOT5图像
小班边界
自动提取
imagery segmentation
SPOT5 imagery
sub-compartment boundary
auto-delineation
