基于HJ-1号卫星数据灌区小麦提取应用研究

及时准确获取灌区小麦分布和面积,可以为流域水量调度、抗旱减灾提供数据支持。本文利用多时相HJ-1号卫星数据分析人民胜利渠灌区小麦生长期影像特征,研究灌区小麦快速提取方法。研究结果表明,利用灌区小麦越冬前多时相HJ-1号卫星影像NDVI数据进行小麦提取,能够准确获取小麦分布和面积信息,精度达到90%以上。     

PTrees: A point-based approach to forest tree extraction from lidar data

This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.     

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.     

An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections

The characterisation the vertical profiles and cross-sections of roads is important for the verification of proper construction and road safety assessment. The goal of this paper is the extraction of geometric parameters through the automatic processing of mobile LiDAR system (MLS) point clouds. Massive and complex datasets provided by the MLS are processed using a hierarchical strategy that includes segmentation, principal component analysis (PCA)-based orthogonal regression, filtering and parameter extraction procedures. Best-fit geometric parameters act as a vertical road model for both linear parameters (slope and vertical curves) and cross-sections (superelevations). The proposed automatic processing approach gives satisfactory results for the analysed scenario.     

深度学习支持下的宅基地复垦项目真实性智能审查技术研究与应用

全域综合整治背景下的宅基地复垦项目审查是保证复垦项目合法性合规性的有效手段,针对当前宅基地复垦项目审查效率低的问题,本文提出了基于遥感影像的农村宅基地复垦项目立项真实性审查方法。首先以重庆市农村宅基地复垦项目为例,建立宅基地复垦项目立项真实性审查模型,利用改进的U-Net模型准确提取建筑物,然后与复垦红线叠加实现复垦项目真实性的智能审查。试验结果表明,该方法能够快速准确地审查复垦项目真实性,对优化土地结构、促进土地流转、实现乡村振兴具有重要意义,可为全域综合整治背景下宅基地复垦项目真实性审查提供技术参考。     

基于移动测量的交通标志自动提取

针对从移动测量车采集的图像中提取交通标志的需求,研究探索适合移动测量的交通标志检测识别方案。在交通标志检测阶段,提出一种检测多个圆形禁令标志连接的多圆度检测法,对于三角形警告标志的检测,在原有拐角检测法基础上提出基于候选区域的拐角检测法;在交通标志识别阶段,选用像素特征作为特征向量,采用概率神经网络实现交通标志的分类识别。最后设计实现移动测量后处理软件的交通标志自动提取模块。     

基于彩色信息尺度不变特征的街景影像匹配

提出了一种基于彩色信息的尺度不变特征匹配算法以实现彩色图像间的特征匹配 引入对光照变化具有一定鲁棒性的对角－偏移模型通过模型变换求出图像各位置的彩色不变量并以此作为输入图像再采用改进的harris算法提取图像的几何信息建立一种具有几何和颜色不变性的特征向量最后利用特征向量间的相关系数完成匹配 本文将该算法用于街景影像匹配中并对比与传统sift算法的差异结果表明此算法不但能够得到分布均匀密集度高的匹配点且耗时少匹配精度高证明了对于街景影像匹配研究的适用性     

Freeman链码优先级直线提取算法研究

针对目前直线提取算法的局限性,提出了一种简单高效的提取图像中目标物体直线边界的算法。本算法基于链码思想和数字直线特征,首先对图像进行边缘检测,对边缘检测后的图像按照优先级进行链码跟踪,获取初始链码;然后通过距离约束提高链码的直线性,剔除噪声和细节;最后进行直线合并,完成直线提取。实验表明,本文提出的算法能高效、准确地检测出图像中物体边界的直线,特别对较大的影像计算量小,抗噪能力强,适用于实时处理。     

用于高分辨率SAR影像建筑物提取的对象级高亮特征描述方法

目的 合成孔径雷达(synthetic aperture radar,SAR)影像分辨率的不断提高为建筑物提取提供了有效的数据支持,而传统像素级方法,提取建筑物的效果较差,精度较低。通过分形网络演化分割算法(fractal net evo-lution approach,FNEA)获取分析单元,利用对象级分析单元与邻近环境之间的上下文特征,提出了高亮邻接强度特征(highlight adjacent intensity,HAI)与亮点散射密度特征(shining point distribute density,SDD)的概念,然后结合上述两种特征进行对象级建筑物的提取。最后通过几组实验验证了基于面向对象特征方法比面向像素特征方法对高分辨率SAR建筑物提取具有更好的效果、更高的精度。