Structure-from-motion photogrammetry analysis of historical aerial photography: Determining beach volumetric change over decadal scales     

Rafael C. Carvalho  David M. Kennedy  Yakufu Niyazi  Chloe Leach  Teresa M. Konlechner  Daniel Ierodiaconou 《地球表面变化过程与地形》2020,45(11):2540-2555

Historical aerial photographs are an invaluable tool in shoreline mapping and change detection in coastal landscapes. We evaluate the extent to which structure-from-motion (SfM) photogrammetric methods can be applied to quantify volumetric changes along sandy beaches, using archival imagery. We demonstrate the application of SfM-derived digital surface models (DSMs) at East Beach and Lady Bay in southwest Victoria, Australia, using photographic datasets taken in 1969, 1977 and 1986, and compare them to LiDAR-derived DSMs acquired at both sites in 2007. The SfM approaches resulted in two entire and two partial suitable DSMs out of six datasets. Good-quality DSMs were spatially continuous with a good spread of ground control points (GCPs) near the beach at Lady Bay, whereas unsuitable DSMs were mostly restricted by poor distribution and number of GCPs in spatially segmented areas of East Beach, due to limited overlapping of images, possible poor quality of GCPs and also the propagation of errors in the derived point clouds. A volume of approximately 223 000 ± 72 000 m³ was deposited at Lady Bay between 1969 and 2007, despite minimal erosion observed near the breakwater. The partially suitable dataset of East Beach indicated that beach erosion of at least 39 m³ m⁻¹ occurred immediately to the east of the seawall after 1977. We also discuss the drawbacks and strengths of SfM approaches as a benchmark of historical erosion assessments along sandy beaches. © 2020 John Wiley & Sons, Ltd.  相似文献   

The potential of Pléiades images with high angle of incidence for reconstructing the coastal cliff face in Normandy (France)     

《International Journal of Applied Earth Observation and Geoinformation》2020

To monitor chalk cliff face along the Normandy coast (NW France) which is prone to erosion, we tested the potential of cliff face 3D reconstruction using pairs of images with high angle of incidence at different dates from the agile Pléiades satellites. The verticality aspect of the cliff face brings difficulties in the 3D reconstruction process. Furthermore, the studied area is challenging mainly because the cliff face is north-oriented (shadow). Pléiades images were acquired over several days (multi-date stereoscopic method) with requested incidence angles until 40°. 3D reconstructions of the cliff face were compared using two software: ASP® and ERDAS IMAGINE®. Our results are twofold. Firstly, despite ASP® provides denser point clouds than ERDAS IMAGINE® (an average of 1.60 points/m² from 40° incidence angle stereoscopic pairs on the whole cliff face of Varengeville-sur-Mer against 0.77 points/m² respectively), ERDAS IMAGINE® provides more reliable point clouds than ASP® (precision assessment on the Varengeville-sur-Mer cliff face of 0.31 m ± 2.53 and 0.39 m ± 4.24 respectively), with a better spatial distribution over the cliff face and a better representation of the cliff face shape. Secondly, the quality of 3D reconstructions depends mostly on the amount of noise from raw images and on the shadow intensity on the cliff face (radiometric quality of images).  相似文献   

基于无人机的用海项目施工期悬浮泥沙监测          下载免费PDF全文

孙芹芹  张加晋  罗美雪 《海洋技术学报》2019,38(3):34-38

为实现对用海项目施工期悬浮泥沙扩散的实时跟踪监测,尝试采用无人机携带可见光、多光谱传感器对古雷围填海项目的围堰区进行遥感探测。利用Pix4d软件对无人机携带的可见光及多光谱照片进行处理,结果表明,可见光及多光谱影像对水体中的悬浮泥沙分布均具有一定的识别能力。通过研究分析多光谱影像的光谱特征发现,近红外波段对悬浮泥沙含量的升高较为敏感,运用波段比值公式计算结果显示,围堰内含沙水体经溢流口排入海域后浓度明显降低,并可向南继续扩散约300 m。该方法可克服遥感卫星影像时空分辨率低的特点,为用海项目资源环境影响跟踪监测提供新的技术手段。  相似文献   

顾及地形起伏的无人机影像覆盖分析方法     

张衡  李骁  叶朋飞 《测绘通报》2020,(1):102-106

使用无人机实施测绘航空摄影时，由于无人机相对航高较低，地面起伏会对无人机影像的分辨率、覆盖范围、重叠度造成较大的影响，影像成果会出现分辨率不足、重叠度不够、覆盖漏洞等缺陷。针对这一情况，本文提出了一种利用数字微分正解法的计算方法，借助DEM准确计算每张影像的覆盖范围，并使用FME软件高效生成全部影像的覆盖范围。经过实际使用，验证了该方法可以在航线设计阶段准确预测并分析全部影像的覆盖范围、重叠度，因此可及时发现设计问题并调整航线。该方法可以有效减少因地形起伏造成的影像覆盖缺陷，减少返工现象，从整体上提高了作业效率。  相似文献   

内点最大化与冗余点控制的无人机遥感图像配准     

余蕊  陈玮扬  杨扬  杨昆  罗毅 《遥感学报》2020,24(11):1325-1341

利用小型无人机进行遥感图像配准在自然灾害损害评估、环境监测和目标检测与追踪等领域发挥着至关重要的作用，但小型无人机的图像采集过程容易受风速/风向、复杂地形、电池容量、飞行姿态、飞行高度等自然或人为因素的影响。这些问题通常会导致捕捉到的场景重叠率低与图像非刚性畸变，在特征点提取过程中产生大量冗余点，增加了图像配准的难度。本文提出一种基于特征点的小型无人机图像配准方法，该方法的核心思想是在配准过程中识别冗余点，同时最大化可用内点数量。所识别的冗余点当作控制点，用于控制网格代图像的运动。最后通过最大化内点和合理移动控制点来恢复图像变换。本文使用50对小型无人机图像进行特征匹配和图像配准的实验，其中平均配准精度可达80.38%，并且本文方法在所有的情况下都优于5种当前流行算法。  相似文献   

空地技术结合地形图测图方法研究     

方秋生  王宵雷  刘如飞  马新江 《测绘与空间地理信息》2020,(3):37-40

车载移动测量系统可以快速、高精度地对测区进行三维激光扫描,但是因地物遮挡、视角限制,使得点云数据存在缺失;无人机航测具有高效率、高灵活性和低成本等优势,但是稳定性差,受天气影像严重,易导致影像不清晰或精度低。无人机航测技术可以弥补车载移动测量技术的采集盲区,后者可以发挥高精度的优点,二者技术联合应用,将极大提高测绘精度及生产效率。本文以某小区为例,进行了相关方法实验,对建筑物顶部或植被茂密处等扫描盲区,采用无人机航测补测,通过高精度激光点云对航摄影像进行纠正匹配,综合利用激光点云与航摄影像进行大比例尺测图。  相似文献   

Monitoring soil surface roughness under growing winter wheat with low-altitude UAV sensing: Potential and limitations     

Nils Onnen  Anette Eltner  Goswin Heckrath  Kristof Van Oost 《地球表面变化过程与地形》2020,45(14):3747-3759

Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd.  相似文献   

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns     

《International Journal of Applied Earth Observation and Geoinformation》2020

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.  相似文献   

空-谱融合的条件随机场高光谱影像分类方法     

魏立飞  余铭  钟燕飞  袁自然  黄灿 《测绘学报》2020,49(3):343-354

高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免“同谱异物”与“同物异谱”的现象方面有较明显的优势。  相似文献   

基于超像元词包特征和主动学习的高分遥感影像变化检测     

杨进一  徐伟铭  王成军  翁谦 《地球信息科学学报》2019,21(10):1594-1607

为解决高分辨率遥感影像变化检测中存在底层特征缺乏语义信息、像元级的检测结果存在“椒盐”现象以及监督分类中样本标注自动化程度较低,本文提出一种基于超像元词包特征和主动学习的变化检测方法。首先采用熵率分割算法获取叠加影像的超像元对象;其次提取两期影像像元点对间的邻近相关影像特征（相关度、斜率和截距）和顾及邻域的纹理变化强度特征（均值、方差、同质性和相异性）,经线性组合作为像元点对的底层特征;然后基于像元点对底层特征利用BOW模型构建超像元词包特征,并采用一种改进标注策略的主动学习方法从无标记样本池中优选信息量较大的样本,且自动标注样本类别;最后训练分类器模型完成变化检测。通过选用2组不同地区的GF-2影像和Worldview-Ⅱ影像作为数据源进行实验,实验结果中2组数据集的F1分数分别为0.8714、0.8554,正确率分别为0.9148、0.9022,漏检率分别为0.1681、0.1868,误检率分别为0.0852、0.0978。结果表明,该法能有效识别变化区域、提高变化检测精度。此外,传统主动学习方法与改进标注策略的主动学习方法的学习曲线对比显示,改进的标注策略可在较低精度损失下,有效提高样本标注自动化程度。  相似文献