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Advances in Remote Sensing 2017年第2期6卷 103-119页

作者： Alaeldin Suliman Yun Zhang Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton Canada

Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.

关键词： Building Detection Very High Resolution Images Optical-Elevation Data co-registration Classification-Based Height Normalization

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation co-registration to Support Building Detection in Off-Nadir VHR Satellite Images

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Journal of Geographic Information System 2018年第1期10卷 25-56页

作者： Alaeldin Suliman Yun Zhang Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton Canada

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation co-registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

关键词： Stereo VHR Satellite Images Off-Nadir Images Disparity Maps Elevation Data co-registration Building Detection Line-of-Sight DSM

Lookup Table Hough Transform for Real Time Range Image Segmentation and Featureless co-registration

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Journal of Sensor Technology 2012年第3期2卷 148-154页

作者： Ben Gorte George Sithole Department of Geomatics University of Cape Town Rondebosch South Africa Department of Geoscience and Remote Sensing Delft University of Technology Delft Netherlands

The paper addresses range image segmentation, particularly of data recorded by range cameras, such as the Microsoft Kinect and the Mesa Swissranger SR4000. These devices record range images at video frame rates and allow for acquisition of 3-dimensional measurement sequences that can be used for 3D reconstruction of indoor environments from moving platforms. The role of segmentation is twofold. First the necessary image co-registration can be based on corresponding segments, instead of corresponding point features (which is common practice currently). Secondly, the segments can be used during subsequent object modelling. By realisising that planar regions in disparity images can be modelled as linear functions of the image coordinates, having integer values for both domain and range, the paper introduces a lookup table based implementation of local Hough transform, allowing to obtain good segmentation results at high speeds.

关键词： Range Camera Range Image Segmentation Hough Transform co-registration

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

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Forest Ecosystems 2022年第5期9卷 674-691页

作者： Reda Fekry Wei Yao Lin Cao Xin Shen Department of Land Surveying and Geo-informatics The Hong Kong Polytechnic UniversityHung HomHong KongChina The Hong Kong Polytechnic University Shenzhen Research Institute ShenzhenChina co-Innovation Center for Sustainable Forestry Nanjing Forestry UniversityNanjing210037China

Light detection and ranging(LiDAR)has contributed immensely to forest mapping and 3D tree modelling.From the perspective of data acquisition,the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels.This research develops a general framework to integrate ground-based and UAV-LiDAR(ULS)data to better estimate tree parameters based on quantitative structure modelling(QSM).This is accomplished in three sequential steps.First,the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy.Next,redundancy and noise were removed for the ground-based/ULS LiDAR data fusion.Finally,tree modeling and biophysical parameter retrieval were based on QSM.Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest,including poplar and dawn redwood species.Generally,ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data.The fusion-derived tree height,tree volume,and crown volume significantly improved by up to 9.01%,5.28%,and 18.61%,respectively,in terms of rRMSE.By contrast,the diameter at breast height(DBH)is the parameter that has the least benefits from fusion,and rRMSE remains approximately the same,because stems are already well sampled from ground data.Additionally,particularly for dense forests,the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR.Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests,whereby the improvement owing to fusion is not significant.

关键词： Ground/aerial view mobile LiDAR Point cloud co-registration Fusion QSM Tree parameter retrieval

FORSAT:a 3D forest monitoring system for cover mapping and volumetric 3D change detection

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International Journal of Digital Earth 2020年第8期13卷 854-885页

作者： Efstratios Stylianidis Devrim Akca Daniela Poli Martin Hofer Armin Gruen Victor Sanchez Martin Konstantinos Smagas Andreas Walli Orhan Altan Elisa Jimeno Alejandro Garcia School of Spatial Planning and Development Aristotle University of ThessalonikiThessalonikiGreece Department of Civil Engineering Isik UniversitySileIstanbulTurkey DiXplorer AG ZurichSwitzerland GeoVille Information Systems GmbH InnsbruckAustria Chair of Information Architecture ETH ZurichSwitzerland Ingeniería Y Soluciones Informáticas S.L. SevillaSpain GeoImaging Ltd NicosiaCyprus Ekinoks Surveying Software Engineering Ltd IstanbulTurkey

A 3D forest monitoring system,called FORSAT(a satellite very high resolution image processing platform for forest assessment),was developed for the extraction of 3D geometric forest information from very high resolution(VHR)satellite imagery and the automatic 3D change detection.FORSAT is composed of two complementary tasks:(1)the geometric and radiometric processing of satellite optical imagery and digital surface model(DSM)reconstruction by using a precise and robust image matching approach specially designed for VHR satellite imagery,(2)3D surface comparison for change detection.It allows the users to import DSMs,align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes(together with precision values)between epochs.FORSAT is a single source and flexible forest information solution,allowing expert and non-expert remote sensing users to monitor forests in three and four(time)dimensions.The geometric resolution and thematic content of VHR optical imagery are sufficient for many forest information needs such as deforestation,clear-cut and fire severity mapping.The capacity and benefits of FORSAT,as a forest information system contributing to the sustainable forest management,have been tested and validated in case studies located in Austria,Switzerland and Spain.

关键词： Deforestation 3D VHR satellite imagery orientation image matching DSM generation co-registration volumetric change detection volume precision

Building Change Detection Improvement Using Topographic correction Models

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Advances in Remote Sensing 2017年第1期6卷 1-22页

作者： Shabnam Jabari Yun Zhang CRC Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton Canada Institution of Remote Sensing and Geographic Information System Peking University Beijing China

In the change detection application of remote sensing, commonly the variation in the brightness values of the pixels/objects in bi-temporal image is used as an indicator for detecting changes. However, there exist effects, other than a change in the objects that can cause variations in the brightness values. One of the effects is the illumination difference on steep surfaces mainly steeproofs of houses in very high resolution images, specifically in off-nadir images. This can introduce the problem of false change detection results. This problem becomes more serious in images with different view-angles. In this study, we propose a methodology to improve the building change detection accuracy using imagery taken under different illumination conditions and different view-angles. This is done by using the Patch-Wise co-registration (PWCR) method to overcome the misregistration problem caused by view-angle difference and applying Topographic correction (TC) methods on pixel intensities to attenuate the effect of illumination angle variation on the building roofs. To select a proper TC method, four of the most widely used correction methods, namely C-correction, Minnaert, Enhanced Minnaert (for slope), and cosine correction are evaluated in this study. The results proved that the proposed methodology is capable to improve the change detection accuracy. Specifically, the correction using the C-correction and Enhanced Minnaert improved the change detection accuracy by around 35% in an area with a large number of steep-roof houses imaged under various solar angles.

关键词： Topographic correction Off-Nadir Imagery Building Change Detection Patch-Wise co-registration (PWCR)