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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Morphological Attribute Profiles With Partial Reconstruction
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Morphological Attribute Profiles With Partial Reconstruction

机译：局部重构的形态学特征

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Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.
机译：扩展属性配置文件（EAP）已被广泛用于高分辨率高光谱图像的分类。 EAP是通过计算一系列属性运算符获得的。属性过滤器（AF）是连接的运算符，因此它们只能通过合并图像的平坦区域来修改图像。这些滤镜在处理非常高分辨率的图像时非常有效，因为它们保留了未从图像中删除的区域的几何特征。然而，作为连接的滤波器的AF遭受“泄漏”的问题（即，与图像中的不同结构有关的，恰好通过虚假链接连接的区域将被视为单个对象）。当与图像中的其他对象连接时，预期会以某个阈值消失的对象仍然存在。小对象的属性将与较大的连接对象混合。在本文中，我们提出了一种用于形态AF的具有部分重构的新颖框架，并将其扩展到高分辨率高光谱图像的分类。提出的框架的最终目标是能够提取空间特征，从而更好地对遥感影像中不同对象的属性建模，从而实现更好的分类性能。所提出的方法的一个重要特征是，它对重新缩放的主成分的范围以及属性值的选择都非常可靠。我们使用各种高光谱图像进行的实验结果表明，所提出的具有部分重建功能的AF框架提供了最新的分类结果。与仅使用单个EAP并将通过现有属性打开和关闭而计算出的所有EAP堆叠在一起的方法相比，所提出的框架在总体分类准确性上有很大的改进。

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