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高空间分辨率遥感影像中地物目标内部光谱信息复杂性的增强,使得传统基于光谱特征值的数据处理方法效果不再显著,影像分割为解决这一问题提供了一种思路,成为当前高空间分辨率遥感影像处理的研究焦点.时刻独立脉冲耦合神经网络具有状态相近、空间相邻神经元相互耦合同步脉冲激发和区域之间神经元脉冲激发时刻独立两大特点,已被应用于非遥感影像分割中,并取得较好效果.本文结合高空间分辨率遥感影像特点,通过对网络参数进行实验和分析,提出一个基于时刻独立脉冲耦合神经网络的高空间分辨率遥感影像分割方法,并利用空间分辨率0.3m的航空影像进行了数据试验,将分割结果进行讨论并与现有时刻独立脉冲耦合神经网络方法和ISODATA方法分割结果进行对比分析.结果表明:时刻独立脉冲耦合神经网络在高空间分辨率遥感影像分割处理中具有很好的应用前景. 相似文献
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利用模式识别中特征提取和特征选择的相关理论对遥感影像的纹理特征进行遴选和变换处理,得到描述纹理的二次特征。实验证明这些新特征能够提高影像分类精度和分类效率。 相似文献
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基于LBV变换的遥感影像多步骤分类法研究 总被引:1,自引:0,他引:1
面对与日俱增的遥感数据,如何快速准确地进行影像分类成为遥感领域亟待解决的问题。本文在前人的基础上提出了一种基于LBV变换的遥感影像多步骤分类方法。L:地物的总辐射水平,集中反映了裸地的信息;B:地物的可见-红外光辐射平衡,是地面水分状况和水体存在的一个良好指标;V:地物辐射随波段变化的方向和速度向量,能集中反映地面植被状况。多步骤分类法遵循由易到难的原则,能克服类别之间的互相影响,从而提高分类精度。文章结合LBV变换和多步骤分类法的优点对影像进行了分类,结果表明该方法简单易行,且能达到良好的分类精度。 相似文献
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R.P. Gupta D.P. Kanungo M.K. Arora S. Sarkar 《International Journal of Applied Earth Observation and Geoinformation》2008
Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures. 相似文献
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Jurassic Tectonics of North China: A Synthetic View 总被引:21,自引:1,他引:20
This paper gives a synthetic view on the Jurassic tectonics of North China, with an attempt to propose a framework for the stepwise tectonic evolution history. Jurassic sedimentation, deformation and magmatism in North China have been divided into three stages. The earliest Jurassic is marked by a period of magmatism quiescence (in 205-190 Ma) and regional uplift, which are considered to be the continuation of the “Indosinian movement” characterized by continent-continent collision between the North and South China blocks. The Early to Middle Jurassic (in 190-170 Ma) was predominated by weak lithospheric extension expressed by mantle-derived plutonism and volcanism along the Yanshan belt and alongside the Tan-Lu fault zone, normal faulting and graben formation along the Yinshan- Yanshan tectonic belt, depression and resuming of coal-bearing sedimentation in vast regions of the North China block (NCB). The Middle to Late Jurassic stage started at 165y.5 Ma and ended up before 136 Ma; it was dominated by intensive intraplate deformation resulting from multi-directional compressions. Two major deformation events have been identified. One is marked by stratigraphic unconformity beneath the thick Upper Jurassic molasic series in the foreland zones of the western Ordos thrust-fold belt and along the Yinshan-Yanshan belt; it was predated 160 Ma. The other one is indicated by stratigraphic unconformity at the base of the Lower Cretaceous and predated 135 Ma. During this last stage, two latitudinal tectonic belts, the Yinshan-Yanshan belt in the north and the Qinling-Dabie belt in the south, and the western margin of the Ordos basin were all activated by thrusting; the NCB itself was deformed by the NE to NNE-trending structural system involving thrusting, associated folding and sinistral strike-slip faulting, which were spatially partitioned. Foliated S-type granitic plutons aged 160-150 Ma were massively emplaced in the Jiao-Liao massif east of the Tan-Lu fault zone and indicate important crustal thicken 相似文献
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A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis 总被引:2,自引:0,他引:2
Yu Liu Qinghua Guo Maggi Kelly 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):461-475
Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. 相似文献
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