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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >MULTILEVEL OBJECT BASED IMAGE CLASSIFICATION OVER URBAN AREA BASED HIERARCHICAL IMAGE SEGMENTATION AND INVARIANT MOMENTS
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MULTILEVEL OBJECT BASED IMAGE CLASSIFICATION OVER URBAN AREA BASED HIERARCHICAL IMAGE SEGMENTATION AND INVARIANT MOMENTS

机译：基于多级对象的基于城市地区的分层图像分割和不变矩的图像分类

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With the availability of very high resolution multispectral imagery from sensors such as IKONOS and Quickbird, it is possible to identify small-scale features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. In the present study, a multilevel object based classification method based on hierarchical segmentation and shape descriptors was proposed. Hierarchical segmentation was first performed by multichannel watershed segmentation and dynamics of the contours in watershed. Traditional watershed transformation defined for gray level image was extended to multispectral image segmentation by computing multispectral gradient image through a vector based approach, which uses extended dilation and erosion operations. The hierarchical multispectral image segmentation was then conducted by an improved method for dynamics of the contours. After segmentation, spectral features and shape features from different segmentation levels were calculated and combined in subsequent classification. The shape features used in the study were the Hu's invariant moments, the useful shape descriptors. A hierarchical object based classification method was proposed, which combined pixel based and object based classification methods. The vegetation classes and shadow area were extracted by pixel based classification and a post classification processing. Non-vegetation classes were classified through an object based classification, which combined the spectral and shape features at multiple scales. The proposed method was compared with several classification methods, using a subset of Quickbird image covering Beijing urban area. The results showed that the proposed method produced higher overall classification accuracy, compared to other classification methods.
机译：随着IKONOS和Quickbird等传感器的非常高分辨率多光谱图像的可用性，可以识别城市环境中的小规模特征。由于城市环境中发现的多尺度特征和不同的土地覆盖类型组成，从高分辨率卫星图像中生产精确的城市陆地覆盖地图是一项艰巨的任务。在本研究中，提出了一种基于分层分割和形状描述符的基于多级对象的分类方法。分层分割首先是由多渠道流域分割和流域轮廓的动态进行。通过通过基于向量的方法计算多光谱梯度图像来扩展为灰度级图像定义的传统流域变换，其使用扩展扩张和侵蚀操作来计算多光谱梯度图像。然后通过用于轮廓的动态的改进方法进行分层多光谱图像分割。在分割之后，计算来自不同分割级别的光谱特征和形状特征，并在随后的分类中组合。该研究中使用的形状特征是HU的不变矩，有用的形状描述符。提出了一种基于分层对象的分类方法，基于像素和基于对象的类别方法。基于像素的分类和后分类处理提取植被类和影子区域。通过基于对象的分类对非植被类进行分类，该分类组合多个尺度的光谱和形状特征。将所提出的方法与若干分类方法进行比较，使用覆盖北京市区的Quickbird图像的子集。结果表明，与其他分类方法相比，该方法的整体分类准确性较高。

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