Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

Rau, Jiann-Yeou; Jhan, Jyun Ping; Hsu, Ya Ching

doi:10.1109/tgrs.2014.2337658

Cited by 74 publications

(53 citation statements)

References 60 publications

(69 reference statements)

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“…To exploit the complementary characteristics of multisource data, data fusion based methods are also popular and have been proven to be more reliable than the single-source data methods used by many researchers [8]. For example, both images and 3D geometry data have been used in several previous studies [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Sun

Lin

Shen

et al. 2017

IJGI

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Abstract:As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Sun

Lin

Shen

et al. 2017

IJGI

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“…Points laying on walls with horizontal normal vectors were used to extract building boundaries, which can be bridged and smoothed to construct building footprints. Rau et al (2015) proposed a method to automatically classify photogrammetric point clouds into various urban object classes. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry and topology related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade.…”

Section: Building Detection From Oblique Imagerymentioning

confidence: 99%

Feature Line Based Building Detection and Reconstruction From Oblique Airborne Imagery

Zhu

Jiang

Zhang

2015

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

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ABSTRACT:In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.

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“…A broader use of data obtained by oblique aerial imaging is presented by Rau et al (2015). In their classification of point clouds from dense image matching of oblique images, the following classes are distinguished: Tree, Grass, Facade, Roof, Road, and Other.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Measurements in Oblique Aerial Images for Urban Environment

Ostrowski

2016

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

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ABSTRACT:Oblique aerial images have been a source of data for urban areas for several years. However, the accuracy of measurements in oblique images during this time has been limited to a single meter due to the use of direct -georeferencing technology and the underlying digital elevation model. Therefore, oblique images have been used mostly for visualization purposes. This situation changed in recent years as new methods, which allowed for a higher accuracy of exterior orientation, were developed. Current developments include the process of determining exterior orientation and the previous but still crucial process of tie point extraction. Progress in this area was shown in the ISPRS/EUROSDR Benchmark on Multi-Platform Photogrammetry and is also noticeable in the growing interest in the use of this kind of imagery. The higher level of accuracy in the orientation of oblique aerial images that has become possible in the last few years should result in a higher level of accuracy in the measurements of these types of images.The main goal of this research was to set and empirically verify the accuracy of measurements in oblique aerial images. The research focused on photogrammetric measurements composed of many images, which use a high overlap within an oblique dataset and different view angles. During the experiments, two series of images of urban areas were used. Both were captured using five DigiCam cameras in a Maltese cross configuration. The tilt angles of the oblique cameras were 45 degrees, and the position of the cameras during flight used a high grade GPS/INS navigation system. The orientation of the images was set using the Pix4D Mapper Pro software with both measurements of the in-flight camera position and the ground control points (measured with GPS RTK technology). To control the accuracy, check points were used (which were also measured with GPS RTK technology).As reference data for the whole study, an area of the city-based map was used. The archived results were referred to image orientation accuracy and to the ground sampling distance of the used images. The results show that the recent development of image orientation methods for oblique aerial images allow these images to be used for high quality photogrammetric measurements.

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Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

Cited by 74 publications

References 60 publications

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

Feature Line Based Building Detection and Reconstruction From Oblique Airborne Imagery

Accuracy of Measurements in Oblique Aerial Images for Urban Environment

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