Cascade multitemporal classification based on fuzzy Markov chains

Feitosa, Raul Queiroz; Costa, Gilson Alexandre Ostwald Pedro da; Mota, Guilherme Lúcio Abelha; Pakzad, Kian; Costa, Maria C.O.

doi:10.1016/j.isprsjprs.2008.09.011

Cited by 19 publications

(8 citation statements)

References 20 publications

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“…Such a strategy is advantageous because the original observations (i.e., the image data) are used for the combination rather than derived data, whereas it is still possible to model the actual type of change. This has, for instance, been done in [9], where a model of temporal dependencies based on Markov chains is applied. Whereas the authors say that their method could be applied to both a pixelwise classification and a segmentwise classification, they only show results for the segmentwise approach.…”

Section: A Related Workmentioning

confidence: 99%

Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery

Hoberg

Rottensteiner

Feitosa

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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In this paper, we present a method for the multitemporal and contextual classification of georeferenced optical remote sensing images acquired at different epochs and having different geometrical resolutions. The method is based on Conditional Random Fields (CRFs) for contextual classification. The CRF model is expanded by temporal interaction terms that link neighboring epochs via transition probabilities between different classes. In order to be able to deal with data of different resolution, the class structure at different epochs may vary with the resolution. The goal of the multitemporal classification is an improved classification performance at all individual epochs, but also the detection of land-cover changes, possibly using lower resolution data. This paper also contains a comparison of the performance of different models for the interaction potentials. Results are given for two different test sites in Germany, where Ikonos, RapidEye, and Landsat images are available. Our results show that the multitemporal classification does indeed increase the overall accuracy of all epochs compared to a monotemporal classification and to a state-of-the-art multitemporal classification method, and that it is feasible to detect changes in lower resolution images.Index Terms-Change detection, conditional random field (CRF), Markov random field (MRF), multiscale, multitemporal classification.

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Section: A Related Workmentioning

confidence: 99%

Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery

Hoberg

Rottensteiner

Feitosa

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…Application schemes based on SVMs are presented by (Nemmour and Chibani, 2006), (He and Laptev, 2009) and (Bovolo et al, 2008). (Mota et al, 2007) and (Feitosa et al, 2009) present fuzzy approaches based on modeling the class transitional probabilities.…”

Section: Change Detection and Multitemporal Classificationmentioning

confidence: 99%

Kernel-Composition for Change Detection in Medium Resolution Remote Sensing Data

Braun

Weidner

Hinz

2012

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

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A framework for multitemporal change detection based on kernel-composition is applied to a multispectral-multitemporal classification scenario, evaluated and compared to traditional change detection approaches. The framework makes use of the fact that images of different points in time can be used as input data sources for kernel-composition-a data fusion approach typically used with kernel based classifiers like support vector machines (SVM). The framework is used to analyze the growth of a limestone pit in the Upper Rhine Graben (West Germany). Results indicate that the highest accuracy rates are produced by the kernel based framework. The approach produces the least number of false positives and gives the most convincing overall impression.

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“…the image data, rather than derived data. This has for instance been done in (Feitosa et al, 2009), where a model of temporal dependencies based on Markov chains is applied. As in most techniques for multitemporal classification, each pixel is classified individually without considering spatial context, which leads to a salt-and-pepperlike appearance of the change detection results.…”

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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Cascade multitemporal classification based on fuzzy Markov chains

Cited by 19 publications

References 20 publications

Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery

Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery

Kernel-Composition for Change Detection in Medium Resolution Remote Sensing Data

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

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