Object-oriented method for urban vegetation mapping using IKONOS imagery
Object-oriented method for urban vegetation mapping using IKONOS imagery
复制标题
使用 IKONOS 图像进行城市植被测绘的面向对象方法
DOI：
10.1080/01431160902882603
复制
发表时间：
2010-01
影响因子：
3.4
通讯作者：
HONG JIANG
中科院分区：
文献类型：
--
作者：
XIUYING ZHANG, XUEZHI FENG;HONG JIANG
城市植被在生活质量中起着重要作用。然而，准确的城市植被图不能很容易地获得单独的多光谱遥感数据，因为光谱波段之间的不同植被类。本研究旨在从IKONOS影像中检测城市植被类别，基于面向对象的方法，可以在分类过程中集成对象的光谱和空间信息，从而可以提高分类能力。针对IKONOS影像中城市植被的特点，设计了一种两尺度分割的方法来获取“目标”，并构建了植被目标的特征集。然后通过相关分析、Jeffries-Matusita（J-M）距离和主成分变换（PCT）去除特征之间的冗余信息。最后，利用分类回归树（CART）模型对植被目标进行识别。结果表明，IKONOS卫星图像可以用于植被类型的制图，总的精度为87.71%。与单一尺度分割相比，宏观尺度和微观尺度的分割可以更好地获取植被对象。结合J-M距离和PCT的相关性分析可以有效地优化特征集。基于规则的分类方法适用于结构复杂的城市植被类型识别。
Urban vegetation plays an important role in quality of life. However, accurate urban vegetation maps cannot be easily acquired from multispectral remotely sensed data alone because the spectral bands are indistinct among different vegetation classes. This study aimed to detect urban vegetation categories from IKONOS imagery based on an object-oriented method that can integrate both spectral and spatial information of objects in the classification procedure and thus can improve classification capability. Considering the characteristics of urban vegetation in IKONOS imagery, a two-scale segmentation procedure was designed to obtain ‘objects’, and the feature set for vegetation objects was constructed. Redundant information among the features was then removed by using correlation analysis, the Jeffries–Matusita (J–M) distance and principal component transformation (PCT). Finally, the vegetation objects were identified by the classification and regression tree (CART) model. The results show that IKONOS imagery can be used to map vegetation types with a total accuracy of 87.71%. Segmentations involving both micro and macro scales could acquire better vegetation objects than using a single scale. The correlation analysis combined with the J–M distance and PCT was efficient in optimizing the feature set. The rule-based classification method is suitable for identifying urban vegetation types using the feature set with a complex structure.
登录
查看更多内容
DOI：
--
发表时间：
2001
期刊：
--
影响因子：
--
作者：
T. Blaschke;J. Strobl
通讯作者：
T. Blaschke;J. Strobl
影响因子：
3.4
作者：
Le Wang;Wayne P. Sousa;Peng Gong
通讯作者：
Le Wang;Wayne P. Sousa;Peng Gong
影响因子：
9.1
作者：
Mathieu, Renaud;Freeman, Claire;Aryal, Jagannath
通讯作者：
Aryal, Jagannath
DOI：
10.1109/igarss.2007.4422786
发表时间：
2007-07
期刊：
2007 IEEE International Geoscience and Remote Sensing Symposium
影响因子：
--
作者：
Bingbing Liu;Soo Chin Liew
通讯作者：
Bingbing Liu;Soo Chin Liew
影响因子：
2.3
作者：
O. Heyman;G. Gaston;A. J. Kimerling;J. Campbell
通讯作者：
O. Heyman;G. Gaston;A. J. Kimerling;J. Campbell