Thermo-acoustic sound generation in the interaction of pulsed proton and laser beams with a water target

The generation of hydrodynamic radiation in interactions of pulsed proton and laser beams with matter is explored. The beams were directed into a water target and the resulting acoustic signals were recorded with pressure sensitive sensors. Measurements were performed with varying pulse energies, sensor positions, beam diameters and temperatures. The obtained data are matched by simulation results based on the thermo-acoustic model with uncertainties at a level of 10%. The results imply that the primary mechanism for sound generation by the energy deposition of particles propagating in water is the local heating of the medium. The heating results in a fast expansion or contraction and a pressure pulse of bipolar shape is emitted into the surrounding medium. An interesting, widely discussed application of this effect could be the detection of ultra-high energetic cosmic neutrinos in future large-scale acoustic neutrino detectors. For this application a validation of the sound generation mechanism to high accuracy, as achieved with the experiments discussed in this article, is of high importance.     

Waveform-based point cloud classification in land-cover identification

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, whereas topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echo and multi-echo classifiers for point cloud classification.The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-path samples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared to an echo-based classifier.     

Operationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery

Quantification of forest degradation in monitoring and reporting as well as in historic baselines is among the most challenging tasks in national REDD+ strategies. However, a recently introduced option is to base monitoring systems on subnational conditions such as prevalent degradation activities. In Tanzania, charcoal production is considered a major cause of forest degradation, but is challenging to quantify due to sub-canopy biomass loss, remote production sites and illegal trade. We studied two charcoal production sites in dry Miombo woodland representing open woodland conditions near human settlements and remote forest with nearly closed canopies. Supervised classification and adaptive thresholding were applied on a pansharpened QuickBird (QB) image to detect kiln burn marks (KBMs). Supervised classification showed reasonable detection accuracy in the remote forest site only, while adaptive thresholding was found acceptable at both locations. We used supervised classification and manual digitizing for KBM delineation and found acceptable delineation accuracy at both sites with RMSEs of 25–32% compared to ground measurements. Regression of charcoal production on KBM area delineated from QB resulted in R²s of 0.86–0.88 with cross-validation RMSE ranging from 2.22 to 2.29 Mg charcoal per kiln. This study demonstrates, how locally calibrated remote sensing techniques may be used to identify and delineate charcoal production sites for estimation of charcoal production and associated extraction of woody biomass.     

长江口EnviSat测高数据的波形分类重构分析

采用波形分类重构算法处理EnviSat卫星从2002年10月至2010年5月在长江口近岸海域28°N~32°N、121°E~125°E范围内的波形数据。该区域内海洋波形、波形后缘前端出现峰值的波形、波形后缘后端出现峰值的波形、似镜面波形和复杂波形分别占89.03%、2.95%、0.45%、3.31%和4.26%。根据不同的波形类别采用不同波形算法进行波形重构。同时,分析了不同重构算法之间的系统偏差,并据此确定OCOG算法、Threshold算法和子波形算法的最优阈值水平分别为65%、45%和50%。重构结果表明,波形分类重构算法优于其他波形重构算法,能有效改善原始海面高的精度,改善程度在16.62%~53.86%之间。此外,重构后交叉点差值小于重构前的交叉点差值,与轨迹P089、P411形成的交叉点的海面高差值由1m降低到25cm左右,其余交叉点的差值均在2~6cm。     

主动学习与图的半监督相结合的高光谱影像分类

针对当前高光谱影像分类时,人工标注样本费时费力以及大量未标记样本未有效利用等问题,提出了一种主动学习与图的半监督相结合的高光谱影像分类方法。首先,将像素的光谱信息与其邻域内的空间信息相结合,利用重排序机制得到一种旋转不变的空谱特征表达。在此基础上,利用主动学习算法选择最不确定性样本(即分类模糊度最大的样本),提交操作者标注得到标记样本集。最后将该标记样本与未标记样本组合,用于图的半监督分类。该算法可保证类别边界样本的选择,利于分类器的边界构造,同时,在较少标记样本情况下,通过引入大量的未标记样本,可以达到较好的分类效果。在3幅真实高光谱影像上的试验表明,该方法可以取得精度较高的分类结果。     

RADARSAT-2全极化SAR数据地表覆盖分类

全极化合成孔径雷达(SAR)能够测量每一观测目标的全散射矩阵,但地物分布的复杂性往往造成不同地物具有相似的后向散射信号特征,因而增加了地物信息提取的难度。文中基于北京地区的RADARSAT-2全极化雷达数据,在图像处理的特征分解的基础上,利用PolSARPro软件提取包含地物散射机理信息的各种极化参数,按H-α、A-α、H-A对全极化SAR影像进行基于散射机理的分类,继而将分类结果作为Wishart H/A/α、Wishart H/α的初始类别划分。最后,采用决策树分类算法对基于Wishart分布的监督分类及以上两种分类算法进行融合处理,从而实现地物的分类,并将分类结果与经典的分类算法进行对比分析,验证了文中方法的有效性。     

跨时空观下的遥感应用新视野

遥感是一门应用性极强的现代地球信息学科,在总结分析遥感学科理论发展与遥感应用新特征基础上,进行遥感学科发展与应用理论探索,是推动遥感学科与应用迈上新台阶的重要任务。本文通过对现有遥感学科组成及分类体系的分析,首先提出了一种基于方法论的遥感学科分类新方法,在分析遥感应用中普遍存在的跨时空特征基础上,从遥感应用新视野提出了跨时空遥感的理论,最后结合典型案例剖析了跨时空遥感的应用。跨时空遥感理论的建立顺应遥感学科发展的需要,遥感应用时空观的提出对今后遥感应用具有参考价值。     

Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。     

硬分类对低空间分辨率图像分类效果的影响

针对图像分类效果与图像的空间分辨率之间的关系尚未得到定量分析的问题,该文以中巴卫星CCD图像对冬小麦的分类结果为参考数据,对空间分辨率与制图精度之间的关系进行理论分析。通过在不同空间分辨率状态下分别计算随着硬分类阈值的取值变化而得到的由漏分误差和错分误差所形成的"帕累托边界",来分析硬分类器所能达到的分类精度的极限。结果表明,在空间分辨率相对较低的前提下,图像整体分类效果取决于被分类像元的纯度在图像中的数量分布状况。研究结论可为制作遥感专题图时如何选取合适的空间分辨率图像提供参考。     

入侵种互花米草的光谱分层分析方法

针对互花米草的爆发式增长对沿海滩涂生物多样性和生态稳定带来了巨大的生态威胁的问题,该文以闽江河口互花米草和其他3种湿地植物的室内叶片高光谱数据为例,探讨互花米草与其伴生植物是否具有光谱可分性。采用一种分层分析方法对实测高光谱数据降维并选择出识别互花米草的最佳波段。首先,利用ANOVA对光谱数据降维,选择出互花米草与其他湿地植物光谱具有显著性差异的波段;其次,使用CART算法对ANOVA降维后具有显著差异的高光谱数据进一步降维,找到识别互花米草潜在的最佳波段;最后,利用J-M距离评估CART选择波段的可分性。结果表明:互花米草与其他3种湿地植物具有光谱可分性,其J-M距离均高于1.9;基于CART算法的入侵种互花米草的识别精度平均达到96.7%,高于传统方法的识别精度。该文成果将为航空或航天高光谱遥感监测互花米草入侵区提供参考。