2017
DOI: 10.1016/j.jag.2017.07.019
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Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Abstract: Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely Discriminative Markov Random Fields with spatio-temporal priors, and Import Vector Machines, in order to advance the mapping of land cover… Show more

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Cited by 19 publications
(9 citation statements)
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“…The random forest model used in Jeong et al [2015] to predict a fluid particle's velocity can be viewed as a transparent choice per se due to its simple nature. Hagensieker et al [2017] classify land use and land cover and their changes based on remote sensing satellite timeseries data. They integrate domain knowledge about the transition of specific land use and land cover classes such as forest or burnt areas to increase the classification accuracy.…”
Section: Group Transparency Interpretabilitymentioning
confidence: 99%
“…The random forest model used in Jeong et al [2015] to predict a fluid particle's velocity can be viewed as a transparent choice per se due to its simple nature. Hagensieker et al [2017] classify land use and land cover and their changes based on remote sensing satellite timeseries data. They integrate domain knowledge about the transition of specific land use and land cover classes such as forest or burnt areas to increase the classification accuracy.…”
Section: Group Transparency Interpretabilitymentioning
confidence: 99%
“…Reasons might be a high inner-class and intra-annual variance, seasonality overall, and possibly limitations concerning the interpretation of the two classes in the TerraClass dataset. Intra-annual variance is of particular interest, as the underlying SAR acquisitions are spread over the entire dry period, which in general also coincide with a decrease of shrubby, in favor of clean pasture [59]. Since these effects are present in the training as well as testing data, classification outcomes are affected to a certain degree.…”
Section: Discussionmentioning
confidence: 99%
“…These areas are rasterized into an image of 5 by 5 m pixel resolution to sample pixels for training and testing. Due to TerraClass being collected based on optical data, which is predominantly available in the dry season between June and September, areas of clean pasture in the reference data can be assumed to be overrepresented due to intra-annual dynamics [59].…”
Section: Reference Datamentioning
confidence: 99%
“…Random Forest, Support Vector Machines) [17][18][19][20][21][22], object-based methodologies [23][24][25], modelling context information by graphical models (e.g. Markov Random Fields, Conditional Random Fields) [26][27][28][29][30][31][32][33][34][35] or automatically learning representations by Neural Networks (e.g. Convolutional or Recurrent Neural Networks) [36][37][38][39][40][41][42].…”
Section: Related Workmentioning
confidence: 99%
“…In [28], Liu et al proposed a spatio-temporal MRF framework for multi-temporal classification and compared a global, a local and a pixel-wise model for temporal interactions to detect changes in forests. Likewise, Hagensieker et al [29] introduced a spatio-temporal MRF for land use/land cover mapping, where the association potential is given by an Import Vector Machines (IVM) classifier and the spatial and temporal interaction potentials are represented by a Potts model and transition matrices from expert knowledge, respectively. In spite of the inclusion of spatial context, MRF based models are limited as the spatial interaction is a function of only labels, disregarding any dependence on the observed data.…”
Section: Related Workmentioning
confidence: 99%