XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [ADDITIONAL PAPERS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: THE BENEFITS OF TERRESTRIAL LASER SCANNING AND HYPERSPECTRAL DATA FUSION PRODUCTS S. J. Buckley, T. H. Kurz, D. Schneider

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia in laser scanning software. The colouring is performed using the EOPs of the image, the geometric model for the sensor and the collinearity condition to obtain 2D pixel coordinates for each 3D lidar point. The 2D position is used to assign RGB values to the 3D points (Fig 4a). Advantages of the method are that a 3D impression of the material distribution can be quickly created, and points not in the geologically relevant parts of the scene can be rapidly highlighted and removed. The point cloud can easily be classified and segmented into the material classes. However, disadvantages are that point clouds are discontinuous (as described in Section 2), and during colouring no account is taken for geometric obstructions in the camera's field of view between a given point and the image plane, making incorrect colouring a possibility when images are not captured from a similar position as the laser scanner. These disadvantages can be alleviated using triangle meshes rather than point clouds — in that case the surface model is continuous, and more occluding geometry can be used to test visibility when colouring the mesh vertices (Fig 4b). Figure 4. Lidar geometry coloured using classification image in Fig. 2. a) point cloud; b) mesh. Height of area e. 15 m. 4.2 Photorealistic modelling with multiple textures A mesh makes it possible to visualise the coloured lidar geometry in a continuous form, aiding ease of interpretation. However, the quality of the result is a function of the number of vertices and triangles in the mesh, where large triangles will cause degradation in detail. Texture mapping relates image pixels to the mesh geometry, making use of the full image resolution independently of mesh detail. During texture mapping, the vertices of each mesh triangle are projected into an appropriate image, defining an area of pixels to be used as texture. All defined patches are saved to one or more texture images and the position of each patch is stored as a property of the vertices in the original mesh. In 3D viewing software, the mesh and texture images are loaded, and the stored texture coordinates define the linkage between vertices and image data, allowing on-screen rendering of the photorealistic model. Hyperspectral classifications can be textured on the lidar model; however, the value is limited by the coarse resolution of the HySpex sensor. Superimposing hyperspectral results on an existing photorealistic model (lidar geometry textured with conventional digital images) provides a far more useful 544 application of texture mapping. This multi-layer approach gives greater context to the thematic maps and is valuable for interpretation and validation. Software for performing the multi-layer texture mapping was implemented, along with 3D viewing software for interactively manipulating the layer combinations. Input data are the mesh model, Nikon imagery, and multiple hyperspectral images and processing products. In addition, a project file stores EOPs of all images as well as a designated layer number that each image should be mapped to. A typical layer configuration is for conventional images to form the first layer, and then hyperspectral results, such as MNF images and MTMF classifications to form subsequent layers. Figure 5. Photorealistic model combining conventional digital imagery and multiple hyperspectral processing layers. Layer 1 is the Nikon imagery (0% transparency) Layer 2 is an MNF image (Fig. 2c), with 5096 transparency, 50% horizontal cutoff and a sharp edge transition. Layer 3 is a classification (Fig. 2d), with 4096 transparency, a horizontal and vertical cutoff, and a soft edge transition. Height of model c. 20 m. The viewing software was written in C++ using the OpenSceneGraph library for 3D rendering, providing a high- level interface to OpenGL. OpenGL supports the use of multiple texture units, termed multitexturing, defining how image textures are stored and used in the graphics system. The number of layers is restricted only by the number of texture units supported by the graphics card of the computer viewing the processed model. During rendering, the layer combination is controlled using OpenGL state properties, making it possible to set the contribution of each texture unit. Most modern graphics cards allow the use of programmable parts of the graphics pipeline. The OpenGL Shading Language offers a high amount of flexibility for multiple textures, as user-defined functions for texture combination can be written and sent to the graphics card as fragment shaders. To take advantage of this, each layer is additionally given a transparency factor (0 to 100%) specifying the overall weighting of the layer. Horizontal and vertical factors are also given, to allow side-by-side visualisation of multiple layers. Each parameter is linked to a slide control in the graphical user interface, allowing interactive adjustment of the layer weighting (Fig. 5). The horizontal and vertical edge transition can be feathered to give hard or soft edges.

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