Semi-automatic Quality Control of Topographic Data Sets

Helmholz, Petra; Becker, Christian; Breitkopf, Uwe; Büschenfeld, Torsten; Busch, Andreas; Braun, Carola; Grünreich, Dietmar; Müller, Sönke; Östermann, Jörn; Pahl, M.; Rottensteiner, Franz; Vogt, Karsten; Ziems, Marcel; Heipke, Christian

doi:10.14358/pers.78.9.959

Cited by 15 publications

(11 citation statements)

References 15 publications

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“…Usually, such methods are used to encode smoothness priors over local neighborhoods (Schindler, 2012), but some works exist that instead of applying smoothness constraints directly encode texture through the prior term (Gimel'farb, 1996;Zhu et al, 1997). Helmholz et al (2012) apply the approach of (Gimel'farb, 1996) to aerial and satellite images as one of several feature extractors inside a semi-automatic quality control tool for topographic datasets.…”

Section: Related Workmentioning

confidence: 99%

Beyond Hand-Crafted Features in Remote Sensing

Tokarczyk¹,

Wegner²,

Walk³

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A basic problem of image classification in remote sensing is to select suitable image features. However, modern classifiers such as AdaBoost allow for feature selection driven by the training data. This capability brings up the question whether hand-crafted features are required or whether it would not be enough to extract the same quasi-exhaustive feature set for different classification problems and let the classifier choose a suitable subset for the specific image statistics of the given problem. To be able to efficiently extract a large quasi-exhaustive set of multi-scale texture and intensity features we suggest to approximate standard derivative filters via integral images. We compare our quasi-exhaustive features to several standard feature sets on four very high-resolution (VHR) aerial and satellite datasets of urban areas. We show that in combination with a boosting classifier the proposed quasi-exhaustive features outperform standard baselines.

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Section: Related Workmentioning

confidence: 99%

Beyond Hand-Crafted Features in Remote Sensing

Tokarczyk¹,

Wegner²,

Walk³

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Helmholz et al (2012), Göpfert et al (2011), Ziems et al (2011), Champion et al (2010, Gerke and Heipke (2008), Rottensteiner (2008) and Eidenbenz et al (2000). These approaches also use 3D information for the verification/update process.…”

Section: Introductionmentioning

confidence: 99%

Approach for the Semi-Automatic Verification of 3d Building Models

Helmholz

Belton

Moncrieff

2013

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

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ABSTRACT:In the field of spatial sciences, there are a large number of disciplines and techniques for capturing data to solve a variety of different tasks and problems for different applications. Examples include: traditional survey for boundary definitions, aerial imagery for building models, and laser scanning for heritage facades. These techniques have different attributes such as the number of dimensions, accuracy and precision, and the format of the data. However, because of the number of applications and jobs, often over time these data sets captured from different sensor platforms and for different purposes will overlap in some way. In most cases, while this data is archived, it is not used in future applications to value add to the data capture campaign of current projects. It is also the case that newly acquire data are often not used to combine and improve existing models and data integrity. The purpose of this paper is to discuss a methodology and infrastructure to automatically support this concept. That is, based on a job specification, to automatically query existing and newly acquired data based on temporal and spatial relations, and to automatically combine and generate the best solution. To this end, there are three main challenges to examine; change detection, thematic accuracy and data matching.

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“…Despite decades of research the degree of automation for map generation and updating still remains low. In practice, most maps are still drawn manually, with varying degree of support from semi-automated tools [Helmholz et al, 2012]. What makes automation particularly challenging for VHR images is that on the one hand their spectral resolution is inherently lower, on the other hand small objects and small-scale surface texture become visible.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Aerial Images With an Ensemble of CNNS

Marmanis

Wegner

Galliani

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

198

143

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ABSTRACT:This paper describes a deep learning approach to semantic segmentation of very high resolution (aerial) images. Deep neural architectures hold the promise of end-to-end learning from raw images, making heuristic feature design obsolete. Over the last decade this idea has seen a revival, and in recent years deep convolutional neural networks (CNNs) have emerged as the method of choice for a range of image interpretation tasks like visual recognition and object detection. Still, standard CNNs do not lend themselves to per-pixel semantic segmentation, mainly because one of their fundamental principles is to gradually aggregate information over larger and larger image regions, making it hard to disentangle contributions from different pixels. Very recently two extensions of the CNN framework have made it possible to trace the semantic information back to a precise pixel position: deconvolutional network layers undo the spatial downsampling, and Fully Convolution Networks (FCNs) modify the fully connected classification layers of the network in such a way that the location of individual activations remains explicit. We design a FCN which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. We discuss design choices and intricacies of such a network, and demonstrate that an ensemble of several networks achieves excellent results on challenging data such as the ISPRS semantic labeling benchmark, using only the raw data as input.

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Semi-automatic Quality Control of Topographic Data Sets

Cited by 15 publications

References 15 publications

Beyond Hand-Crafted Features in Remote Sensing

Beyond Hand-Crafted Features in Remote Sensing

Approach for the Semi-Automatic Verification of 3d Building Models

Semantic Segmentation of Aerial Images With an Ensemble of CNNS

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