Normalizing shadows in multi-temporal aerial frame imagery using relative radiometric adjustments to support near-real-time change detection

This study addresses the problem of shadows in multi-temporal imagery, which is a key issue with change detection approaches based on image comparison. We apply image-to-image radiometric normalizations including histogram matching (HM), mean-variance (MV) equalization, linear regression based on pseudo-invariant features (PIF-LR), and radiometric control sets (RCS) representing high- and low-reflectance extrema, for the novel purpose of normalizing brightness of transient shadows in high spatial resolution, bi-temporal, aerial frame image sets. Efficient shadow normalization is integral to remote sensing procedures that support disaster response efforts in a near-real-time fashion, including repeat station image (RSI) capture, wireless data transfer, shadow detection (as precursor to shadow normalization), and change detection based on image differencing and visual interpretation. We apply the normalization techniques to imagery of suburban scenes containing shadowed materials of varied spectral reflectance characteristics, whereby intensity (average of red, green, and blue spectral band values) under fully illuminated conditions is known from counterpart reference images (time-1 versus time-2). We evaluate the normalization results using stratified random pixel samples within transient shadows, considering central tendency and variance of differences in intensity relative to the unnormalized images. Overall, MV equalization yielded superior results in our tests, reducing the radiometric effects of shadowing by more than 85 percent. The HM and PIF-LR approaches showed slightly lower performance than MV, while the RCS approach proved unreliable among scenes and among stratified intensity levels. We qualitatively evaluate a shadow normalization based on MV equalization, describing its utility and limitations when applied in change detection. Application of image-to-image radiometric normalization for brightening shadowed areas in multi-temporal imagery in this study proved efficient and effective to support change detection.     

徕卡RTC360三维激光扫描仪在建筑立面改造中的应用

徕卡RTC360极速三维激光扫描仪作为徕卡一款新型设备,不仅操作简单、扫描速度快,可在尽可能短的时间内完成海量丰富点云和高清影像的采集,数据全面,细节丰富,极大地提高了外业的工作效率;而且非常智能。本文采用VIS视觉追踪技术,通过对扫描仪位置进行跟踪定位,在采集过程中进行点云的实时拼接,无需标靶、公共点和人工干预;现场实时查看点云预拼接,极大减少了内业工作量;后期搭配Cyclone Register360智能拼接软件,点云无需人工干预,可实现自动智能拼接处理,以及外业采集及内业数据处理的简单高效。     

基于图像处理的异重流与系泊结构物作用模型试验研究

异重流广泛存在于江河入海口附近,速度快、破坏力强,给近海工程的施工作业带来不小的难度。为了研究系泊结构物在异重流作用下的运动响应,基于图像处理技术,采用开闸式异重流模型试验方法,将异重流和结构物作为研究对象。利用图像处理技术处理由高速摄像机拍摄的结构物运动图片,从而获得结构物运动的时历曲线。对比讨论了不同密度比的异重流对结构物运动的影响,同一密度比的异重流对处于不同深度结构物运动的影响。研究结果表明：结构物运动受异重流密度的影响较大,结构物的运动幅值随着密度的增大而增大。处于高密度流体非混合层的结构物受到异重流的作用会产生大幅度的顺时针旋转和正x方向偏移;位置较高的方柱受到淡水的主导作用,会产生逆时针旋转和负x方向偏移。位于盐水与淡水剧烈混合区域的方柱并不会产生单一方向的偏移,而是在平衡位置附近波动。     

国产走航式声学海流剖面仪的应用分析

针对国产走航式声学海流剖面仪的应用性能进行评价,为用户及生产厂家提供借鉴。首先,就国产走航式声学海流剖面仪的实际数据,从不同角度和层面,全面分析和考察了其测量数据的各项性能,有针对性地绘制了各类图件,并通过数据质量、数据有效性、测流精度、测流深度等各方面的分析比对,对国产走航式声学海流剖面仪做出定性评价。其次,通过与国外同类仪器的以往应用情况进行对比分析,发现在船只转向和往复航行、加速和减速航行、高速航行时,国产走航式声学海流剖面仪表现更佳。最后,基于应用中的不足给出相关建议。     

资源三号02星激光测高数据质量分析

近几年来,中国的对地观测卫星激光测高技术发展较快,资源三号02星上搭载了国内首台对地观测的卫星激光测高试验载荷,在后续的高分七号、陆地生态系统碳监测卫星上均装备业务化应用的激光测高仪,开展国产对地观测卫星激光测高数据质量分析研究非常必要,能有效填补国内空白并推动相关技术发展。本文对资源三号02星载荷的激光测高数据进行了严密几何处理,重点从夜间观测、卫星小角度侧摆、海面区域等不同条件下的数据质量进行了分析,对数据的平均可用率进行了统计分析。试验表明,资源三号02星激光测高仪能获得约30%有效测高数据,在夜间数据有效利用率略有提高,在大型水面也能获得有效数据,经后处理后在平坦地区验证的绝对高程精度优于1.0 m,部分点高程精度优于0.5 m,硬件本身的测距精度和卫星姿态测量误差是主要的误差源。相关结论对于后续国产卫星激光测高载荷的研制以及数据后处理和应用具有参考价值。     

基于Pix4Dmapper的无人机低空摄影测量数据处理

随着科技发展,无人机的使用就像汽车一样应用于各个领域,成熟的航空摄影测量理论为无人机在测量方面的使用提供了坚实的基础,而后处理成像软件通过提供三维模型来实现数据的精确可视化,更进一步提高了无人机的有效性。本文利用航空摄影测量的中心投影原理,提出基于Pix4Dmapper的无人机低空摄影测量数据处理流程,对经过空中三角测量和相机校准的航摄像片经过光束法区域网平差、空三加密、点云编辑、滤波处理,生成高精度的Google瓦片、正射影像图(DOM)、三维点云模型、数字表面模型(DSM)、数字高程模型(DEM)、数字地面模型(DTM),经过多次应用表明:处理成果能够满足应用需求,极大促进了摄影测量技术的发展。     

Parallel computing solutions for Markov chain spatial sequential simulation of categorical fields

The Markov chain random field (MCRF) model is a spatial statistical approach for modeling categorical spatial variables in multiple dimensions. However, this approach tends to be computationally costly when dealing with large data sets because of its sequential simulation processes. Therefore, improving its computational efficiency is necessary in order to run this model on larger sizes of spatial data. In this study, we suggested four parallel computing solutions by using both central processing unit (CPU) and graphics processing unit (GPU) for executing the sequential simulation algorithm of the MCRF model, and compared them with the nonparallel computing solution on computation time spent for a land cover post-classification. The four parallel computing solutions are: (1) multicore processor parallel computing (MP), (2) parallel computing by GPU-accelerated nearest neighbor searching (GNNS), (3) MP with GPU-accelerated nearest neighbor searching (MP-GNNS), and (4) parallel computing by GPU-accelerated approximation and GPU-accelerated nearest neighbor searching (GA-GNNS). Experimental results indicated that all of the four parallel computing solutions are at least 1.8× faster than the nonparallel solution. Particularly, the GA-GNNS solution with 512 threads per block is around 83× faster than the nonparallel solution when conducting a land cover post-classification with a remotely sensed image of 1000?×?1000 pixels.     

Research on Wide-field Imaging Techniques for Low-frequency Radio Arraytwo

Wide-field imaging of low-frequency radio telescopes is subject to a number of difficult problems. One particularly pernicious problem is the non-coplanar baseline effect. It will lead to distortion of the final image when the phase of $w$-direction called $w$-term is ignored. The image degradation effects are amplified for the telescopes with a wide field of view. This paper summarizes and analyzes several $w$-term correction methods and their technical principles. Their advantages and disadvantages are analyzed after comparing their computational cost and computational complexity. We conduct simulations with two of these methods, i.e., faceting and $w$-projection, based on the configuration of the first-phase Square Kilometre Array (SKA) low-frequency array. The resulted images are also compared with the result of the two-dimensional Fourier transform method. The results show that the image quality and correctness derived from both faceting and $w$-projection are better than the two-dimensional Fourier transform method in wide-field imaging. The effects of the number of facets and the $w$-direction step length on the image quality and running time are evaluated. The results indicate that the number of facets and the $w$-direction step length must be reasonable. Finally, we analyze the effect of data size on the running times of the faceting and $w$-projection algorithms. The results show that the faceting and $w$-projection algorithms need to be optimized before the huge amount of data processing. The research of the present paper initiates the analysis of wide-field imaging techniques and their applications in the existing and future low-frequency arrays, and will foster their applications in even broader fields.     

新时代煤炭资源全生命周期地质保障技术体系

新时代对煤炭行业提出了新要求,带来了新挑战、新机遇。在总结煤炭学者最新研究成果的基础上,中国煤炭地质总局提出了煤炭资源全生命周期的自主论述,将新时代煤炭资源全生命周期划分为绿色勘查、绿色开发、清洁利用和恢复绿色四个阶段,探讨了各阶段地质技术的相互关系、技术重点与特征,构建了新时代煤炭资源全生命周期地质技术工作框架,展望了主要攻关方向,为促进煤炭行业“安全、高效、绿色、智能”发展提供了地质保障技术思路。     

Volcano video data characterized and classified using computer vision and machine learning algorithms

Video cameras are common at volcano observatories,but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual analysis.For cameras to serve as effective monitoring tools,video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable activity.In this study,we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations.Data were collected at Villarrica Volcano,Chile from two visible band cameras located～17 km from the vent that recorded at 0.1 and 30 frames per second between February and April 2015.Over these two months,Villarrica exhibited a diverse range of eruptive activity,including a paroxysmal eruption on 3 March.Prior to and after the eruption,activity included nighttime incandescence,dark and light emissions,inactivity,and periods of cloud cover.We quantify the color and spatial extent of plume emissions using a blob detection algorithm,whose outputs are fed into a trained artificial neural network that categorizes the observable activity into five classes.Activity shifts from primarily nighttime incandescence to ash emissions following the 3 March paroxysm,which likely relates to the reemergence of the buried lava lake.Time periods exhibiting plume emissions are further analyzed using a row and column projection algorithm that identifies plume onsets and calculates apparent plume horizontal and vertical rise velocities.Plume onsets are episodic,occurring with an average period of～50 s and suggests a puffing style of degassing,which is commonly observed at Villarrica.However,the lack of clear acoustic transients in the accompanying infrasound record suggests puffing may be controlled by atmospheric effects rather than a degassing regime at the vent.Methods presented here offer a generalized toolset for volcano monitors to classify and track emission statistics at a variety of volcanoes to better monitor periods of unrest and ultimately forecast major eruptions.