A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images

Zhong, Ping; Wang, Runsheng

doi:10.1109/tgrs.2007.907109

Cited by 165 publications

(92 citation statements)

References 28 publications

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“…In this approach, the matrix globally assumes stationary class transitions over all pixels neglecting changes that may exist in the image (Liu et al, 2008). In addition, MRF's assumption of conditional independence in observed data adopted for computational tractability neglect spatial context inherence in images (Lafferty et al, 2001;Kumar, 2006;Zhong and Wang, 2007a;Parikh and Batra, 2008). Remotely sensed images exhibit a coherent scene because neighbouring sites are spatially correlated.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

Kenduiywo

Bargiel

Soergel

2015

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

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ABSTRACT:The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while global demand for food has grown twofold. There is need to monitor and update agriculture land-cover to support food security measures. This study develops a spatial-temporal approach using conditional random fields (CRF) to classify co-registered images acquired in two epochs. We adopt random forest (RF) as CRF association potential and introduce a temporal potential for mutual crop phenology information exchange between spatially corresponding sites in two epochs. An important component of temporal potential is a transitional matrix that bears intra-and inter-class changes between considered epochs. Conventionally, one matrix has been used in the entire image thereby enforcing stationary transition probabilities in all sites. We introduce a site dependent transition matrix to incorporate phenology information from images. In our study, images are acquired within a vegetation season, thus perceived spectral changes are due to crop phenology. To exploit this phenomena, we develop a novel approach to determine site-wise transition matrix using conditional probabilities computed from two corresponding temporal sites. Conditional probability determines transitions between classes in different epochs and thus we used it to propagate crop phenology information. Classification results show that our approach improved crop discrimination in all epochs compared to state-of-the-art mono-temporal approaches (RF and CRF monotemporal) and existing multi-temporal markov random fields approach by Liu et al. (2008).

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

Kenduiywo

Bargiel

Soergel

2015

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

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“…In the last decade, a large number of automatic built-up area detection approaches have been reported [1,2,3,4,5,6,7,8], which can be roughly divided into two categories. The first category of such methods rely on supervised classification.…”

Section: Introductionmentioning

confidence: 99%

“…The first category of such methods rely on supervised classification. More precisely, they use a large set of specific training samples to learn the feature distribution of built-up areas for detection, see [1,2]. The second category of methods directly detect built-up areas without using any training data.…”

Section: Introductionmentioning

confidence: 99%

A perception-inspired building index for automatic built-up area detection in high-resolution satellite images

Liu

Xia

Huang

et al. 2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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This paper addresses the problem of automatic extraction of built-up areas from high-resolution remote sensing images. We propose a new building presence index from the point view of perception. We argue that built-up areas usually result in significant corners and junctions in high-resolution satellite images, due to the man-made structures and occlusion, and thus can be measured by the geometrical structures they contained. More precisely, we first detect corners and junctions by relying on a perception-inspired corner detector, called an a-contrario junction detector. Each detected corner is associated with a perceptual significance, which measures the structural saliency of the corner in the image and is independent of the contrast and scale. All these detected corners together with their significance are then used to compute the building index. The proposed approach is evaluated on a high-resolution satellite image set, including 15 big images from GeoEye-1, QuickBird and IKONOS. The results demonstrated that our method achieves the state-of-the-art results and can be used in practical applications.

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“…In remote sensing CRF have been used for monotemporal classification, e.g. of settlement areas in HR optical satellite images (Zhong & Wang, 2007) or crop types and other land cover classes in Landsat data (Roscher et al, 2010). Multitemporal classification based on CRF for improving the overall classification accuracy as well as detecting changes has first been applied in (Hoberg et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Context Models for CRF-Based Classification of Multitemporal Remote Sensing Data

Hoberg

Rottensteiner

Heipke

2012

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

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ABSTRACT:The increasing availability of multitemporal satellite remote sensing data offers new potential for land cover analysis. By combining data acquired at different epochs it is possible both to improve the classification accuracy and to analyse land cover changes at a high frequency. A simultaneous classification of images from different epochs that is also capable of detecting changes is achieved by a new classification technique based on Conditional Random Fields (CRF). CRF provide a probabilistic classification framework including local spatial and temporal context. Although context is known to improve image analysis results, so far only little research was carried out on how to model it. Taking into account context is the main benefit of CRF in comparison to many other classification methods. Context can be already considered by the choice of features and in the design of the interaction potentials that model the dependencies of interacting sites in the CRF. In this paper, these aspects are more thoroughly investigated. The impact of the applied features on the classification result as well as different models for the spatial interaction potentials are evaluated and compared to the purely label-based Markov Random Field model.

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A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images

Cited by 165 publications

References 28 publications

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

A perception-inspired building index for automatic built-up area detection in high-resolution satellite images

Context Models for CRF-Based Classification of Multitemporal Remote Sensing Data

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