Application of multispectral LiDAR to automated virtual outcrop geology

Terrestrial laser scanning (TLS) is a valuable tool for creating virtual 3D models of geological outcrops to enable enhanced modeling and analysis of geologic strata. Application of TLS data is typically limited to the geometric point cloud that is used to create the 3D structure of the outcrop model. Digital photography can then be draped onto the 3D model, allowing visual identification and manual spatial delineation of different rock layers. Automation of the rock type identification and delineation is desirable, and recent work has investigated the use of terrestrial hyperspectral photography for this purpose. However, passive photography, whether visible or hyperspectral, presents several complexities, including accurate spatial registration with the TLS point cloud data, reliance on sunlight for illumination, and radiometric calibration to properly extract spectral signatures of the different rock types. As an active remote sensing method, a radiometrically calibrated TLS system offers the potential to directly provide spectral information for each recorded 3D point, independent of solar illumination. Therefore, the practical application of three radiometrically calibrated TLS systems with differing laser wavelengths, thereby achieving a multispectral dataset in conjunction with 3D point cloud data, is investigated using commercially available hardware and software. The radiometric calibration of the TLS intensity values is investigated and the classification performance of the multispectral TLS intensity and calibrated reflectance datasets evaluated and compared to classification performed with passive visible wavelength imagery. Results indicate that rock types can be successfully identified with radiometrically calibrated multispectral TLS data, with enhanced classification performance when fused with passive visible imagery.     

成象光谱遥感图象的有限光谱混合分析

本文提出了一种成象光谱数据处理的新方法──有限光谱混合分析方法ＣＳＭＡ（ＣｏｎｓｔｒａｉｎｅｄＳｐｅｃｔｒａｌＭｉｘｔｕｒｅＡｎａｌｙｓｉｓ）,该方法将图象中每一象素看作多种类别地物的混合,利用成象光谱仪各波段反射值计算各个类别所占的比例,这是一个求解混合光谱方程最优解的过程。本文提出的有限光谱混合分析方法是在约束条件下求混合光谱方程的最优解,并提出了快速算法。该方法可以得到比传统遥感分类更有意义的结果,使遥感技术更趋于定量化、实用化。     

基于光谱相似性的多光谱数据纹理编码算法研究

基于多光谱纹理“映射模式”概念，提出了基于光谱数据相似性的多光谱、高光谱数据的编码方法。利用光谱相似测度对不同类型的纹理进行编码，表征地物的全局纹理特征，将纹理提取的算法扩展到多维光谱图像分析中，提出了多尺度纹理组合算法。试验证明，该方法合理有效，可大大提高分类的准确性和精度。     

多光谱浅海水深提取方法研究

利用我国南海某岛礁的TM数据和实测水深资料,试验性地研究了一种在不同底质反射条件下多光谱定量提取水深信息的方法,计算了浅海岛礁水深,取得了较好的应用效果和较高的测深精度.     

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity.     

多光谱遥感电视系统

本文提出了一种利用增强型ＣＣＤ电视摄像技术,实现在可见光与近红外波段内的多光谱遥感成像。采用该技术可适时获取窄带光谱内的多光谱图像,从而为农业生产提供出更多的多光谱图像信息。     

The selection of appropriate spectrally bright pseudo-invariant ground targets for use in empirical line calibration of SPOT satellite imagery

The appropriate utilization of multi-temporal SPOT multispectral satellite imagery in quantitative remote sensing studies requires the removal of atmospheric effects. One widely used and potentially very accurate way of achieving absolute atmospheric correction is the calibration of at-satellite radiance data to field measures of the surface reflectance factor (ρ_s). There are a number of variations in this technique, which are known collectively as empirical line (EL) approaches. However, the successful application of an EL spectral calibration requires the presence and careful selection of appropriate pseudo-invariant ground targets within each scene area. Real surfaces, even those that are man-made and vegetation-free, display non-Lambertian reflectance behaviour to some extent. Because of the ±31° off-nadir incidence angle range of the SPOT sensors, this is a crucial consideration. In favourable circumstances, it may be possible to utilize a goniometer to collect multiangular ρ_s measurements, but for widespread lower cost application of EL approaches currently, the use of a handheld spectrometer to measure nadir only ρ_s is a more realistic proposition. In either case, the selection of targets that have more limited and stable multiangular reflectance behaviour is preferable. Details are given of the reflectance properties of a variety of spectrally bright potential calibration surface types, encompassing sands, gravel, asphalts, and managed and artificial grass turf surfaces, measured in the field using the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). Bright calibration site selection requirements for SPOT data are discussed and the physical mechanisms behind the varying reflectance characteristics of the surfaces are considered. The most desirable properties for useful calibration targets are identified. The results of this study will assist other workers in the identification of likely suitable EL calibration sites for medium and high resolution optical satellite data, and therefore help optimize efforts in the time consuming and costly process of measuring ρ_s in the field.     

基于局部光谱空间分析的多尺度边缘检测

分析了传统边缘检测算法在多光谱影像应用中的局限性，提出了一种基于局部区域的光谱空间平均半径测度的边缘检测方法，该方法扩展了原有的单波段影像边缘检测的概念，将像元的灰度信息转化为光谱矢量，提高了检测的可靠性，并给出了多尺度边缘组合的算法。通过MAIS成像光谱仪对鄱阳湖30个波段数据试验，证明了此方法对边缘检测的有效性。     

A six-camera digital video imaging system sensitive to visible,red edge,near-infrared,and mid-infrared wavelengths

This paper describes a six-camera multispectral digital video imaging system designed for natural resource assessment and shows its potential as a research tool. It has five visible to near-infrared light sensitive cameras, one near-infrared to mid-infrared light sensitive camera, a monitor, a computer with a multichannel digitizing board, a keyboard, a power distributor, an amplifier, and a mouse. Each camera is fitted with a narrowband interference filter, allowing the system to obtain imagery in the blue (447 – 455 nm), green (555 – 565 nm), red (625 – 635 nm), red edge (704 – 716 nm), near-infrared (814–826 nm), and mid-infrared (1631 – 1676 nm) regions of the electromagnetic spectrum. Analogue video acquired by this system is converted to digital format. Radiometric resolution of the imagery is 8-bit (pixel values range 0 – 255). Images obtained by the system can be evaluated individually and/or in combination with each other to assess natural resources.     

Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.     

Remote spectral imaging with simultaneous extraction of 3D topography for historical wall paintings

PRISMS (Portable Remote Imaging System for Multispectral Scanning) is designed for in situ, simultaneous high resolution spectral and 3D topographic imaging of wall paintings and other large surfaces. In particular, it can image at transverse resolutions of tens of microns remotely from distances of tens of metres, making high resolution imaging possible from a fixed position on the ground for areas at heights that is difficult to access. The spectral imaging system is fully automated giving 3D topographic mapping at millimetre accuracy as a by-product of the image focusing process. PRISMS is the first imaging device capable of both 3D mapping and spectral imaging simultaneously without additional distance measuring devices. Examples from applications of PRISMS to wall paintings at a UNESCO site in the Gobi desert are presented to demonstrate the potential of the instrument for large scale 3D spectral imaging, revealing faded writing and material identification.     

Estimating mineral content of indigenous browse species using laboratory spectroscopy and sentinel-2 imagery

Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R² = 0.72, p < 0.05) for winter and (R² = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R² = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales.     

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the “Litto3D” coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.     

协同遥感信息与统计信息的人口空间格局分析

针对人口空间格局分布分析不足的问题,借助遥感信息与基本统计信息,引入人口地理集中度、探索性空间数据分析和地理加权回归模型,定量地分析了特征因子在市域范围上的空间异质性。人口集中指数与Moran指数能有效地反映空间事物之间的密切作用关系,在产业空间分布中能较好地评价集聚效应,反映空间分布状态。以长江经济带为研究区域,对人口空间分布进行了研究,得出了“圈层集中-东西对立-南北差异-四周分散”的分布特征,并从经济、社会、环境、土地利用方面分析原因,为人口合理增长和资源分配提供了参考。