Building change detection with RGB-D map generated from UAV images

Chen, Baohua; Chen, Zhixiang; Deng, Lei; Duan, Yueqi; Zhou, Jie

doi:10.1016/j.neucom.2015.11.118

Cited by 51 publications

(18 citation statements)

References 31 publications

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“…This is typically accomplished with structure-from-motion (SfM) photogrammetry [25,26], which is a process for extracting geometric structures from camera images taken from different camera stations. General steps for the 3D reconstruction are as follows: (1) feature point extraction using the scale-invariant feature transform (SIFT) algorithm [27]; (2) feature matching [28]; (3) applying the SfM algorithm [29] and a bundle block adjustment [30] to recover the image poses [31] and build sparse 3D feature points; (4) building dense 3D point clouds from camera poses estimated from Step (3) and sparse 3D feature points using the multi-view stereo (MVS) algorithm [31]; and (5) building a 3D polygonal mesh of the object surface based on the dense cloud. Good results from the SfM algorithm rely significantly on the quality (proper image texture, image block geometry, divergence, etc.)…”

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

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Abstract:Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed Remote Sens. 2017, 9, 642; doi:10.3390/rs9070642 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 642 2 of 21 method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

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Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

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“…Additionally, the depth standard deviation (DSTD) descriptor (Eq. (4)) is adopted to measure the variance of depth within the local area around a point (Chen et al, 2016). If the local area is de ned by voxel,…”

Section: Change Detection Methodologymentioning

confidence: 99%

Urban area change visualization and analysis using high density spatial data from time series aerial images

Altuntas

2019

Reports on Geodesy and Geoinformatics

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Urban changes occur as a result of new constructions or destructions of buildings, extensions, excavation works and earth fill arising from urbanization or disasters. The fast and efficient detection of urban changes enables us to update geo-databases and allows effective planning and disaster management. This study concerns the visualization and analysis of urban changes using multi-period point clouds from aerial images. The urban changes in the city centre of the Konya Metropolitan area within arbitrary periods between the years 1951, 1975, 1998 and 2010 were estimated after comparing the point clouds by using the iterative closest point (ICP) algorithm. The changes were detected with the point-to-surface distances between the point clouds. The degrees of the changes were expressed with the RMSEs of these point-to-surface distances. In addition, the change size and proportion during the historical periods were analysed. The proposed multi-period change visualization and analysis method ensures strict management against unauthorized building or excavation and more operative urban planning.

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“…A subsequent work in Tian et al [18] adopted Dempster-Shafer fusion theory to combine height changes derived by DSM and the Kullback-Liebler divergence similarity measure between the original images to extract real building changes. In addition, rule-based classification [19][20][21], decision-tree [22], graph cuts [23], and random forest [24] were also used to fuse multiple features to achieve building change detection. These methods consider both geometric and spectral information at the same time, and in the algorithm framework it is easy to combine other information sources for change detection [10].…”

Section: Introductionmentioning

confidence: 99%

Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

Pang

Zhang

et al. 2019

Remote Sensing

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Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

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Building change detection with RGB-D map generated from UAV images

Cited by 51 publications

References 31 publications

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Urban area change visualization and analysis using high density spatial data from time series aerial images

Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

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