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The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data 总被引:2,自引:0,他引:2
Peter Bunting 《Remote sensing of environment》2006,101(2):230-248
In mixed-species forests of complex structure, the delineation of tree crowns is problematic because of their varying dimensions and reflectance characteristics, the existence of several layers of canopy (including understorey), and shadowing within and between crowns. To overcome this problem, an algorithm for delineating tree crowns has been developed using eCognition Expert and hyperspectral Compact Airborne Spectrographic Imager (CASI-2) data acquired over a forested landscape near Injune, central east Queensland, Australia. The algorithm has six components: 1) the differentiation of forest, non-forest and understorey; 2) initial segmentation of the forest area and allocation of segments (objects) to larger objects associated with forest spectral types (FSTs); 3) initial identification of object maxima as seeds within these larger objects and their expansion to the edges of crowns or clusters of crowns; 4) subsequent classification-based separation of the resulting objects into crown or cluster classes; 5) further iterative splitting of the cluster classes to delineate more crowns; and 6) identification and subsequent merging of oversplit objects into crowns or clusters. In forests with a high density of individuals (e.g., regrowth), objects associated with tree clusters rather than crowns are delineated and local maxima counted to approximate density. With reference to field data, the delineation process provided accuracies > ∼70% (range 48-88%) for individuals or clusters of trees of the same species with diameter at breast height (DBH) exceeding 10 cm (senescent and dead trees excluded), with lower accuracies associated with dense stands containing several canopy layers, as many trees were obscured from the view of the CASI sensor. Although developed using 1-m spatial resolution CASI data acquired over Australian forests, the algorithm has application elsewhere and is currently being considered for integration into the Definiens product portfolio for use by the wider community. 相似文献
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遥感监测是开展区域水土流失动态监测的重要手段。对同一地区不同时期遥感影像进行影像分类,对比分析两期分类结果可以实现对土地利用等水土流失影响因子的动态监测。传统方法通常采用人工目视勾绘法获得土地利用分类结果,耗时耗力且效率不高。以同一地区不同时期的遥感影像为对象,基于eCognition软件平台,采用多尺度分割和面向对象分类方法快速获取了影像分类结果。结果表明,该方法分类精度较高,能有效提高工作效率。 相似文献
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基于eCogniton的高分辨率遥感图像的自动识别分类技术 总被引:10,自引:0,他引:10
传统的遥感信息分类和提取,主要是利用数理统计与人工解译相结合的方法.这种方法不仅精度相对较低,效率不高,而且依赖参与解译分析的人,在很大程度上不具备重复性.专业高分辨率遥感影像分类软件eCogniton采用一种全新的面向对象图像的分类技术来进行影像的分类和信息提取.面向对象图像分类技术的关键技术在于:(1)用来解译图像的信息并不在单个像元中,而是在图像对象和其相互关系中;(2)eCogniton采用多分辨率对象分割方法生成图像对象,提高了分类信息的信噪比;(3)基于对象的分类技术不同于纯粹的光谱信息分类,图像对象还包含了许多的可用于分类的一些其他特征:形状、纹理、相互关系、上下关系等信息.eCogniton的分类结果与传统分类方法相比。其特征提取算子更加地适合于几何信息和结构信息丰富的高分辨率图像的自动识别分类. 相似文献
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多尺度分割方法占用系统资源多、耗时长,并且传统面向对象的分类方法是基于全景影像和已分割好的对象进行分类的,其分类结果需要人工加以完善.针对上述问题,提出一种基于像元和面向对象相结合的高分辨率影像信息提取方法.该方法利用原始QuickBird卫星影像创建一个低分辨率影像的子工程进行对象的粗糙分类,并逐个深入分析对象区域.在此基础上,将提取出来的对象轮廓进行规范化处理.最后用大面积区域的影像进行了库塘提取实验,结果表明,该方法不仅提高了信息提取的效率,而且提取的总体效果较好. 相似文献
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大熊猫生境状况关系到大熊猫的长期生存与繁衍, 从大尺度来评价大熊猫生境状况有利于生境的保护与保护区的规划与建设, 从而有利于大熊猫的保护。在大熊猫生物学与行为生态学的研究成果基础上, 通过广泛的野外调查, 在地理信息系统(GIS) 和遥感(RS) 技术支持下, 利用大熊猫生境结构理论模型, 选取海拔、坡度、植被类型、竹子分布等评价因子, 系统地研究了秦岭山系大熊猫生境的分布、生境质量与空间格局、以及生境保护现状, 并提出了相应的生境保护对策。研究表明:①秦岭山系的大熊猫生境面积约为44 万hm 2, 80% 的生境分布于海拔1 500~ 2 400 m 之间; ②由于交通、河流以及沿途的开发建设, 整个生境被分为大小不等的若干部分; ③当前的大熊猫保护区与拟建的保护区及走廊带之间互相连接, 形成了一个较为完整的保护区体系, 保护了70% 以上的大熊猫生境。研究结果能为该山系的大熊猫生境保护提供依据。 相似文献
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