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

Hoberg, Thorsten; Rottensteiner, Franz; Feitosa, Raul Queiroz; Heipke, Christian

doi:10.1109/tgrs.2014.2326886

Cited by 81 publications

(53 citation statements)

References 36 publications

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“…In that regard, hidden Markov models (HMMs) [15] and conditional random fields (CRFs) [16] have shown promising classification accuracies with multi-temporal data. However, the underlying Markov property limits long-term learning capabilities, as Markov-based approaches assume that the present state only depends on the current input and one previous state.…”

Section: Related Workmentioning

confidence: 99%

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Rußwurm

Körner

2018

IJGI

246

184

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Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of today's sensors enable the use of temporal, along with spectral and spatial features.Domains such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results by using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells that reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, our experiments achieved state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing, compared to other classification approaches.

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Section: Related Workmentioning

confidence: 99%

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Rußwurm

Körner

2018

IJGI

246

184

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“…Satellite data provides information to perform classification with respect to different land use, based on hyperspectral analyses. (Hoberg et al, 2015) present a multitemporal and multiscale classification based on Conditional Random Fields (CRF). As well as there are several approaches to perform building outline detection from satellite imagery (Niemeyer et al, 2014).…”

Section: Urban Classificationmentioning

confidence: 99%

Processing of Crawled Urban Imagery for Building Use Classification

Tutzauer¹,

Haala²

2017

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

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ABSTRACT:Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

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“…There are several multi-layer CRF approaches making use of pair-wise potentials (Kosov et al, 2013;Hoberg et al, 2015;Yang and Förstner, 2011). In our previous work, we have proposed a two-layer CRF for the classification of land cover and land use, where the statistical dependencies between land cover and land use are modelled explicitly by pair-wise potentials (Albert et al, 2014b).…”

Section: Related Workmentioning

confidence: 99%

An Iterative Inference Procedure Applying Conditional Random Fields for Simultaneous Classification of Land Cover and Land Use

Albert

Rottensteiner

Heipke

2015

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

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ABSTRACT:Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

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

Cited by 81 publications

References 36 publications

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Processing of Crawled Urban Imagery for Building Use Classification

An Iterative Inference Procedure Applying Conditional Random Fields for Simultaneous Classification of Land Cover and Land Use

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