NESTED-HIERARCHICAL SCENE MODELS AND IMAGE SEGMENTATION
NESTED-HIERARCHICAL SCENE MODELS AND IMAGE SEGMENTATION
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DOI：
10.1080/01431169208904109
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发表时间：
1992-11-10
影响因子：
3.4
通讯作者：
HARWARD, VJ
中科院分区：
文献类型：
--
作者：
WOODCOCK, C;HARWARD, VJ
遥感图像分割的目的是在图像中确定与地面场景中的目标相对应的区域。传统的场景模型的图像分割过程中假设，在图像中表现的对象具有内部的变化，这是低的和平等的。这个场景模型简单得不切实际。另一种场景模型识别场景中不同比例的对象。层次中的每个级别都是嵌套的，或者由上一级别的对象或对象类别组成。不同的对象可能具有不同的属性，允许放宽等方差的假设。多通道、基于区域的分割算法改进了从更好地建模为嵌套层次的场景中分割图像的效果。多遍方法允许区域缓慢而谨慎地增长，同时区域间距离低于全局阈值。超过全局阈值，最小区域尺寸参数迫使区域在高局部方差的区域中发展。最大和可行的区域大小参数限制了不必要的大区域的开发。在陆地卫星TM图像中应用分割算法进行林分划定，可以产生与景观中可识别特征相对应的区域。使用一个本地的方差，自适应窗口纹理通道结合光谱带，提高了能力，以定义区域对应的稀疏库存的森林看台，具有较高的内部方差。
The objective of image segmentation in remote sensing is to define regions in an image that correspond to objects in the ground scene. Traditional scene models underlying image segmentation procedures have assumed that objects as manifest in images have internal variances that are both low and equal. This scene model is unrealistically simple. An alternative scene model recognizes different scales of objects in scenes. Each level in the hierarchy is nested, or composed of objects or categories of objects from the preceding level. Different objects may have distinct attributes, allowing for relaxation of assumptions like equal variance.A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modelled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while inter-region distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions.Application of the segmentation algorithm for forest stand delineation in Landsat TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely-stocked forest stands which have high internal variance.