Building Detection From One Orthophoto and High-Resolution InSAR Data Using Conditional Random Fields

Wegner, Jan Dirk; Hänsch, Ronny; Thiele, Antje; Soergel, Uwe

doi:10.1109/jstars.2010.2053521

Cited by 79 publications

(41 citation statements)

References 18 publications

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“…However, MRF estimates the joint distribution of labels and data, and it involves a distribution of data that is always difficult to achieve. Additionally, likelihood in one site is obtained only from the single site, but not all sites, and the prior term only compares adjacent sites [9,10]. In contrast, the global contextual information is taken into account in CRF to model the posterior probability of labels [11,12].…”

Section: Sar and Road Network Extractionmentioning

confidence: 99%

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Fan

et al. 2017

IJGI

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This paper introduces an innovative road network extraction algorithm using synthetic aperture radar (SAR) imagery for improving the accuracy of road extraction. The state-of-the-art approaches, such as fraction extraction and road network optimization, failed to obtain continuous road segments in separate successions, since the optimization could not change the parts ignored by the fraction extraction. In this paper, the proposed algorithm integrates the fraction extraction and optimization procedure simultaneously to extract the road network: (1) the Bayesian framework is utilized to transfer the road network extraction to joint reasoning of the likelihood of fraction extraction and the priority of network optimization; (2) the multi-scale linear feature detector (MLFD) and the network optimization beamlet are introduced; (3) the conditional random field (CRF) is used to reason jointly. The result is the global optimum since the fraction extraction and network optimization are exploited at the same time. The proposed algorithm solves the problem that the fractions are bound to reduce in the process of network optimization and has demonstrated effectiveness in real SAR images applications.

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Section: Sar and Road Network Extractionmentioning

confidence: 99%

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Fan

et al. 2017

IJGI

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“…It directly models the posterior probability of the given observation data so that the contextual information can be considered in both the observed data and the labeled data. As a context classification model, Kumar and Hebert [27] successfully extended this model to two-dimensional image classification and processing in 2003, and the CRF method has also been successfully applied in hyperspectral imagery classification [28,29], man-made scene interpretation [30], building detection from high-resolution interferometric synthetic aperture radar (InSAR) data [31], change detection [32], and high-resolution image classification, integrating the spectral, spatial, and location information by adding the additional higher-order potential in the pairwise CRF model [33].…”

Section: Conditional Random Fields (Crf)mentioning

confidence: 99%

Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery

et al. 2017

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High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spectral-emissivity land-cover classification based on conditional random fields). A spectral-spatial feature set is constructed considering the spectral variability and spatial-contextual information, to extract features from the high-resolution visible image. The emissivity is retrieved from the thermal infrared hyperspectral image by the FLAASH-IR algorithm and firstly introduced in the fusion of the visible and thermal infrared hyperspectral imagery; also, the emissivity is utilized in SSECRF, which contributes to improving the identification of man-made objects, such as roads and roofs. To complete the land-cover classification, the spatial-spectral feature set and emissivity are integrated by constructing the SSECRF energy function, which relates labels to the spatial-spectral-emissivity features, to obtain an improved classification result. The classification map performs a good result in distinguishing some certain classes, such as roads and bare soil. Also, the experimental results show that the proposed SSECRF algorithm efficiently integrates the spatial, spectral, and emissivity information and performs better than the traditional methods using raw radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137.

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“…We have to cope with different imaging geometries and 3D effects, different spectral properties, viewing angles and spatial resolution, multi-temporal differences etc., and have to combine image space (2D) with 3D processing and fusion in a common coordinate system. Some first investigations on combination of optical and SAR images for building detection are given in Wegner et al (2011). Figure 6.…”

Section: Data Fusion and Change Detectionmentioning

confidence: 99%

Rapidmap – rapid mapping and information dissemination for disasters using remote sensing and geoinformation

Baltsavias

Cho

Remondino

et al. 2013

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

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ABSTRACT:This paper will present the project RAPIDMAP. The project is part of CONCERT-Japan, an ERA-NET initiative funded through the FP7 INCO project frame for enhancing research cooperation between European countries and Japan on two topics, one of which is Resilience Against Disasters. The project started in June 2013 and has a duration of 2 years. In the paper, we will outline the aims of the project, methodologies and techniques to be developed and some test data. Remote Sensing (RS) and Geographic Information System (GIS) are powerful technologies for collecting useful information on the damages of disasters in short time. However, since many different types of RS data are available (satellite, aerial, UAV, terrestrial), data co-registration, information integration and feature extraction need reliable and advanced methodologies. In the RAPIDMAP project, we will develop practical ways to integrate RS data processing tools in near-real-time and allow users to use this data soon after the disasters by means of WebGIS tools. This will help not only decision makers but also end-users in the disaster area. The key components of this project are: (1) Near-real-time monitoring: the procedure of near-real-time monitoring with satellites as well as Unmanned Airborne Vehicles (UAV) will be set up and demonstrated. (2) Data co-registration: in disasters, various images as well as maps come from different sources. The co-registration of multiple images is a key technology for information integration. In this project, a system to co-register multiple images in near-real-time will be developed. (3) Data fusion and change detection: one of the advantages of RS is to collect information with multiple sensors. Various methods for fusing optical with active microwave (SAR) sensor data for information extraction and change detection will be developed. (4) Decision Support System (DSS) based on WebGIS technologies: the collected and integrated information has to be easily accessible and visible by decision makers and end-users in near-real-time and worldwide. By using WebGIS technologies, wireless networks and portable terminals, a DSS will allow easy access, retrieval and visualization of all information (fused data, images, maps, etc.) in very short time after data collection and processing. The project will be practically tested and demonstrated at the Tohoku area in Japan and another test site, which were recently affected by large disasters.

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Building Detection From One Orthophoto and High-Resolution InSAR Data Using Conditional Random Fields

Cited by 79 publications

References 18 publications

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Spatial-Spectral-Emissivity Land-Cover Classification Fusing Visible and Thermal Infrared Hyperspectral Imagery

Rapidmap – rapid mapping and information dissemination for disasters using remote sensing and geoinformation

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