Information from imagery: ISPRS scientific vision and research agenda

Chen, Jun; Dowman, I. J.; Li, Songnian; Li, Zhilin; Madden, Marguerite; Mills, J. P.; Paparoditis, Nicolas; Rottensteiner, Franz; Sester, Monika; Toth, Charles K.; Trinder, John C.; Heipke, Christian

doi:10.1016/j.isprsjprs.2015.09.008

Cited by 86 publications

(63 citation statements)

References 202 publications

(174 reference statements)

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“…This is due to the fact that segmentation is sensitive to many factors, like image sensor resolution, scene complexity or number of bands [6]. Object-based classification of VHR images represents a viable mobility, health, as well as safety and security and for this we need to deliver high-quality spatial information (i.e., timely, complete, reliable, geometrically and thematically accurate) [40]. Speed and accuracy are important factors when dealing with big remote sensing data or time conditioned events (e.g., earthquakes, tsunamis, landslides, refugees, tornados, oil spills etc.)…”

Section: Introductionmentioning

confidence: 99%

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Csillik

2017

Remote Sensing

110

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Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint.

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Section: Introductionmentioning

confidence: 99%

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Csillik

2017

Remote Sensing

110

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“…The ISPRS scientific research agenda reported in Chen et al (2015) identified an ongoing need for benchmarking in photogrammetry (Commission II) and open geospatial science in spatial information science (Commission IV). Recent efforts toward this goal include work by Rottensteiner et al (2014), Nex et al (2015), Campos-Taberner et al (2016), Koch et al (2016), and Wang et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Metric Evaluation Pipeline for 3d Modeling of Urban Scenes

Bosch

Leichtman

Chilcott

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Publicly available benchmark data and metric evaluation approaches have been instrumental in enabling research to advance state of the art methods for remote sensing applications in urban 3D modeling. Most publicly available benchmark datasets have consisted of high resolution airborne imagery and lidar suitable for 3D modeling on a relatively modest scale. To enable research in larger scale 3D mapping, we have recently released a public benchmark dataset with multi-view commercial satellite imagery and metrics to compare 3D point clouds with lidar ground truth. We now define a more complete metric evaluation pipeline developed as publicly available open source software to assess semantically labeled 3D models of complex urban scenes derived from multi-view commercial satellite imagery. Evaluation metrics in our pipeline include horizontal and vertical accuracy and completeness, volumetric completeness and correctness, perceptual quality, and model simplicity. Sources of ground truth include airborne lidar and overhead imagery, and we demonstrate a semi-automated process for producing accurate ground truth shape files to characterize building footprints. We validate our current metric evaluation pipeline using 3D models produced using open source multi-view stereo methods. Data and software is made publicly available to enable further research and planned benchmarking activities.

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“…Chen et al [17] observe that more attention is required to calibration for precise geometric measurement tasks in image metrology and for unconventional imaging devices. Efforts have previously been made in the calibration of various digital-zoom lens devices, such as computer controllable lens systems [18,19], single-lens reflex cameras [16,20], still video cameras [21,22], pan-tilt-zoom (PTZ) systems [23,24], and industrial cameras [25].…”

Section: Related Workmentioning

confidence: 99%

A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration

et al. 2017

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Abstract:Compared with prime lenses, zoom lenses have inherent advantages in terms of operational flexibility. Zoom lens camera systems have therefore been extensively adopted in computer vision where precise measurement is not the primary objective. However, the variation of intrinsic camera parameters with respect to zoom lens settings poses a series of calibration challenges that have inhibited widespread use in close-range photogrammetry. A flexible zoom lens calibration methodology is therefore proposed in this study, developed with the aim of simplifying the calibration process and promoting practical photogrammetric application. A zoom-dependent camera model that incorporates empirical zoom-related intrinsic parameters into the collinearity condition equations is developed. Coefficients of intrinsic parameters are solved in a single adjustment based on this zoom lens camera model. To validate the approach, experiments on both optical-and digital-zoom lens cameras were conducted using a planar board with evenly distributed circular targets. Zoom lens calibration was performed with images taken at four different zoom settings spread throughout the zoom range of a lens. Photogrammetric accuracies achieved through both mono-focal and multi-focal triangulations were evaluated after calibration. The relative accuracies for mono-focal triangulations ranged from 1: 6300 to 1: 18,400 for the two cameras studied, whereas the multi-focal triangulation accuracies ranged from 1: 11,300 to 1: 16,200. In order to demonstrate the applicability of the approach, calibrated zoom lens imagery was used to render a laser-scanned point cloud of a building façade. Considered alongside experimental results, the successful application demonstrates the feasibility of the proposed calibration method, thereby facilitating the adoption of zoom lens cameras in close range photogrammetry for a wide range of scientific and practical applications.

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Information from imagery: ISPRS scientific vision and research agenda

Abstract: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence Newcastle University ePrints-eprint.ncl.ac.uk

Cited by 86 publications

References 202 publications

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

Metric Evaluation Pipeline for 3d Modeling of Urban Scenes

A Flexible, Generic Photogrammetric Approach to Zoom Lens Calibration

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