摘要
通过对影像进行光谱特征分析,及对各种植被类型进行物候特征分析,选用以NDVI数据为主的多波段、多时相的MODIS影像数据进行最小噪声分离MNF变换,然后进行灰值形态学滤波,运用阈值分割法提取旱地,并运用自组织特征映射SOM神经网络聚类模型分离湿地和水田。实验结果与现有的研究成果相比,精度有较大提高。
By analyzing spectral characteristics and phenological characteristics of remote sensing images,we select multi-temporal NDVI data along with other useful bands for data processing.By performing an enhanced Lee filter and MNF transform on the data,the features of different land use types has been enhanced.Then,a morphological filtering is conducted on the data to extract dry land.Finally,we separate wetland and paddy field by a SOM neural network clustering.As a result,the accuracy of land use type classification has been improved.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2011年第2期153-156,I0003,I0003,共6页
Geomatics and Information Science of Wuhan University
基金
国家重点基础研究发展计划资助项目(2006CB701305)
资源与环境信息系统国家重点实验室自主创新团队计划资助项目(088RA400SA)
