Alignment of Hyperspectral Imagery and Full-Waveform Lidar Data for Visualisation and Classification Purposes

Miltiadou, Milto; Warren, Mark; Grant, Michael; Brown, Matthew

doi:10.5194/isprsarchives-xl-7-w3-1257-2015

Cited by 9 publications

(11 citation statements)

References 15 publications

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“…Decomposing means extracting peaks from the waveform, which are caused by surfaces reflecting the laser beam. DASOS [79] is a software developed for aligning FW data and hyperspectral imagery for classification purposes [80,81]. It features three main processing steps: (i) creating 3D meshes, (ii) calculating 2D metrics from both lidar and hyperspectral imagery, (iii) creating feature vectors for further classification.…”

Section: Full-waveform Processing Toolsmentioning

confidence: 99%

Open software and standards in the realm of laser scanning technology

Pirotti

2019

Open geospatial data, softw. stand.

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This review aims at introducing laser scanning technology and providing an overview of the contribution of open source projects for supporting the utilization and analysis of laser scanning data. Lidar technology is pushing to new frontiers in mapping and surveying topographic data. The open source community has supported this by providing libraries, standards, interfaces, modules all the way to full software. Such open solutions provide scientists and end-users valuable tools to access and work with lidar data, fostering new cutting-edge investigation and improvements of existing methods. The first part of this work provides an introduction on laser scanning principles, with references for further reading. It is followed by sections respectively reporting on open standards and formats for lidar data, tools and finally webbased solutions for accessing lidar data. It is not intended to provide a thorough review of state of the art regarding lidar technology itself, but to provide an overview of the open source toolkits available to the community to access, visualize, edit and process point clouds. A range of open source features for lidar data access and analysis is provided, providing an overview of what can be done with alternatives to commercial end-to-end solutions. Data standards and formats are also discussed, showing what are the challenges for storing and accessing massive point clouds. The desiderata are to provide scientists that have not yet worked with lidar data an overview of how this technology works and what open source tools can be a valid solution for their needs in analysing such data. Researchers that are already involved with lidar data will hopefully get ideas on integrating and improving their workflow through open source solutions.

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Section: Full-waveform Processing Toolsmentioning

confidence: 99%

Open software and standards in the realm of laser scanning technology

Pirotti

2019

Open geospatial data, softw. stand.

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“…Pan-sharpening algorithms are also used in other applications such as object detection and classification [23], [24]. Recently, fusion of HSI and Light Detection And Ranging (LiDAR) images has also been studied [25], [26]. To the best of our knowledge, no research has been performed so far to visualize an HSI data by fusion it with an HRCI.…”

Section: Introductionmentioning

confidence: 99%

A Manifold Alignment Approach for Hyperspectral Image Visualization With Natural Color

Liao¹,

Qian²,

Zhou³

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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Trichromatic visualization of hundreds of bands in a hyperspectral image has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display hyperspectral images in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing hyperspectral image with natural color by fusion of a hyperspectral image and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high dimensional spectral space of the hyperspectral image and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the hyperspectral image. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the hyperspectral image and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other hyperspectral images acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user specified bands as natural color images, which is very helpful for users to scan bands of interest.

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“…The waveforms samples were inserted into a 3D voxelised space and the voxels were visualised using different transparencies according to their intensity. Similarly, Miltiadou et al [13] adopted voxelisation for 3D polygonal model creation and applied it to larger areas. Once the 3D density volume is generated, an algebraic object is defined.…”

mentioning

confidence: 99%

“…As the depth of the tree increases, the length of the parallelised instruction increases as well and therefore a good speed up is achieved. Nevertheless, the function for the implicit representation of the FW LiDAR data at [13] executes in constant time, making it harder to achieve speed up using SIMD machines. Further, according to the C++ Coding Standards when optimisation is required, it is better to seek an algorithmic approach first because it is simpler to maintain and less likely to contain bugs [24].…”

mentioning

confidence: 99%

“…It worth highlighting that this paper investigates the performance of six data structures perform on iso-surface extraction (3D polygonal model creation) of voxelised full-waveform LiDAR data. Even though the performance of the algorithm is tested on the algorithm implemented by the authors at [13], there are many distinct and acknowledged approaches on 3D forest modelling and segmentation [25]. Except for forest related applications, 3D modelling was applied and has also been applied on urban environments [26,27].…”

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confidence: 99%

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A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

et al. 2021

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In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

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Alignment of Hyperspectral Imagery and Full-Waveform Lidar Data for Visualisation and Classification Purposes

Cited by 9 publications

References 15 publications

Open software and standards in the realm of laser scanning technology

Open software and standards in the realm of laser scanning technology

A Manifold Alignment Approach for Hyperspectral Image Visualization With Natural Color

A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

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