Classification of multitemporal remote sensing data of different resolution using Conditional Random Fields

Hoberg, Thorsten; Rottensteiner, Franz; Heipke, Christian

doi:10.1109/iccvw.2011.6130248

Cited by 7 publications

(8 citation statements)

References 24 publications

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“…More recently, Conditional Random Fields (CRF) were introduced to avoid the problems of MRFs with oversmoothing in areas where the image content changes abruptly [6]. In remote sensing, CRF have been used for the detection of buildings in optical and SAR images [14], for the classification of optical satellite images [4], and for the generation of a Digital Terrain Model (DTM) from airborne laserscanner data [8]. In this paper we propose a new method for the classification of scenes containing crossroads as a first step of a 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

3D classification of crossroads from multiple aerial images using conditional random fields

Kosov¹,

Rottensteiner²,

Heipke³

2012

7th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)

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We apply Conditional Random Fields for the classification of scenes containing crossroads, using a simple appearance-based model in combination with a probabilistic model of the co-occurrence of class labels at neighbouring image sites. We use multiple overlap aerial images to derive a digital surface model and a true orthophoto without dynamic objects such as cars. An evaluation on an urban data set of aerial images delivers promising results.

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Section: Introductionmentioning

confidence: 99%

3D classification of crossroads from multiple aerial images using conditional random fields

Kosov¹,

Rottensteiner²,

Heipke³

2012

7th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)

Self Cite

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“…Methods for multitemporal image analysis can be grouped into three main categories (Hoberg et al, 2011). The first one is related to the classification of single images based on a single powerful classifier or on a combination of classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Approaches that take the temporal dependencies into account usually model temporal interaction by class transition matrices that can be determined by an expert (Hoberg et al, 2010) (Hoberg et al, 2011) empirically from existing data sources, * Corresponding author or computed statistically (Leite et al, 2011) (Kenduiywo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

Diaz¹,

Feitosa²,

Sanches³

et al. 2016

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

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ABSTRACT:This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

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“…TM s(t)s(k) is a temporal transition matrix similar to the transition probability matrix in (Bruzzone et al, 2004 (Hoberg et al, 2011).…”

Section: Temporal Interaction Potentialmentioning

confidence: 99%

“…A combination of multitemporal and multiscale analysis of remote sensing data using CRF is presented by Hoberg et al (2011). A set of multispectral images of different resolution is classified simultaneously in order to increase the accuracy and reliability of the classification results and to detect land cover changes between the individual epochs.…”

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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Classification of multitemporal remote sensing data of different resolution using Conditional Random Fields

Cited by 7 publications

References 24 publications

3D classification of crossroads from multiple aerial images using conditional random fields

3D classification of crossroads from multiple aerial images using conditional random fields

A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

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

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