基于阈值分割与决策树的SAR影像水体信息提取

目前我国的GF-3 SAR数据可实现全天时全天候的对地观测,已服务于海洋、减灾、水利、气象等多个领域.改进了基于阈值分割法与决策树的GF-3 SAR影像水体信息提取方法,首先对GF-3 SAR影像进行基本处理;再采用KI二值化阈值分割法进行图像分割;然后通过构建知识决策树模型来提取水体信息,为了提高精度,采用GDEM数...     

针对倾斜建筑物的四分量分解方法

经典的Freeman-Durden三分量分解模型存在着体散射能量过度估计的现象,这种现象易导致倾斜建筑物(即建筑物的排列方向与雷达飞行方向不平行)被错误解译为森林.虽然存在多种分解方法来避免倾斜建筑物的错误解译,但从非反射对称散射的角度来应对体散射能量过度估计目前研究仍较少.因此,提出一种新的四分量分解方法,可有效抑制...     

小卫星遥感影像自动提取水体方法研究

基于小卫星影像的水体信息提取方法,即用波段运算得到特征波段(PRWI),经过决策树等辅助手段提取出水体信息。通过在山东省微山湖上级坝北湖选取水体所做的试验表明,该方法在平原地区的总体提取效果较好,经检验发现其误判的像元主要是位于水体和其他地物的交界处。在山区选取山东省东周水库做的试验表明,在山区用该方法提取的小卫星水体信息和Landsat卫星的TM/ETM影像差距不大。小卫星还可以很好地用于城市规划、生态环境、土地利用和环境灾害等动态监测。     

虚拟现实中基于Direct3D树的图片生成方法

树的表达一直是人们很感兴趣的一个研究内容,本文基于PC机平台采用VC和Direct3D技术给出了在虚拟场景中创建可漫游的树的一种简单生成方法,实现虚拟场景中树的漫游的模拟,达到了虚拟现实的效果,同时为自然界其它自然物体的模拟提供了一定的设计思想。     

基于决策树的交通信号灯智能调整算法研究

交通信号灯的智能化研究是智能交通系统(ITS)的一个重要研究领域。通过交通信号灯智能化的调整,可以对道路交通进行有效的无人化疏导,既节省了警力,又提高了效率。交通信号灯时间的调整,采样数据量大,并有很强的随机性。因此,本文基于数据挖掘技术,提出了一种学习模型——决策树的算法,作为交通信号灯智能化调整的依据,并通过一个路口的实际检测,取得了良好的效果,证明是一种简捷可行的方法。     

基于像斑的多光谱影像跨尺度分类研究

提出了一种新的多尺度像斑模型,充分利用多尺度像斑模型所提供的尺度纵向信息,并结合决策树的分类方法来实现跨尺度分类,而不直接进行最佳尺度选择。实验结果证明,跨尺度方法较单一尺度分类能更准确地区分地物,从而提高分类精度。     

基于高分辨率遥感影像的DSM建筑物点的提取研究

采用基于小面积去除方法的中值SUSAN噪声点平滑方法,结合高分辨率遥感影像,对DSM中房屋点的提取进行了探讨。实验表明,本方法能有效地从DSM中提取绝大部分建筑物点,有助于建筑物的精确三维重建。     

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m² plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.     

The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery

Logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel- and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel- and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon’s Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel- and object-based image classifications.     

青藏高原冰雪覆盖变化监测的卫星立体影像方法

针对青藏高原冰雪覆盖变化监测问题,以各拉丹冬冰川为例,提出了利用ALOS立体像对提取冰雪DEM,并以同源不同时相的冰雪DEM监测冰雪覆盖变化及体量变化的方法。依据DEM纠正裁切后的影像,采用ISODATA分类方法得到了冰雪覆盖面积变化量,由不同时相网格DEM高程点数据高差与面积求得体积变化,得出了2009年与2010年冬季冰雪量大小。结果显示2010年12月比2009年12月冰雪量减少了19.728 3 km³,雪覆盖面积减少了349.691 km²。文中还列出了遇到的技术困难及有待进一步研究的问题。