MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images

Grinias, I.; Panagiotakis, Costas; Tziritas, Georgios

doi:10.1016/j.isprsjprs.2016.10.010

Cited by 103 publications

(58 citation statements)

References 44 publications

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“…The authors in [1] propose a novel segmentation algorithm based on a Markov random field model. In addition, they present a novel feature-selection approach useful for detecting both roads and buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…• MRF method [1]: is based on Markov random field model segmentation. This segmentation algorithm is based on class-driven vector data quantization and clustering and the estimation of the likelihoods given the resulting clusters.…”

Section: The Sztaki-inria Datasetmentioning

confidence: 99%

“…It is worth mentioning that BDSV records the lowest FO and MO values, thus outperforming all the benchmarked algorithms with an overall F-score of 0.925. The authors of [1] use pixel-level accuracy assessment to compare their unsupervised building and road classification algorithm against existing ones in the literature. Pixel-level performance benchmark and accuracy assessment rely on the precision (Pr) metric defined earlier in Equation (9), recall (Rc) shown in Equation (6), and the F1-score defined in Equation (8).…”

Section: The Sztaki-inria Datasetmentioning

confidence: 99%

“…Moreover, we benchmarked our proposed approach with state-of-the-art building detection algorithms such as Markov random field model segmentation [1], and the multi temporal marked point processes proposed in [2]. Results reveal that BDSV records the highest overall quality percentage (92%), in addition to the lowest execution time.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Building Detection Using Edge Properties and Image Color Invariants

Ghandour

Jezzini

2018

Buildings

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Automated building extraction from high-resolution satellite imagery is a challenging research problem, and several issues remain with respect to the variety of variables to be accounted for. In this paper we present an approach for building detection using multiple cues. We use the shadow, shape, and color features of buildings to propose our approach, known as Building Detection with Shadow Verification (BDSV). BDSV has three main pillars, which are: (1) tile building detection (TBD) to detect roof tile buildings; (2) flat building detection (FBD) to detect non-tile flat buildings according to shape features; and (3) results fusion used to fuse and aggregate results from previous blocks. Analyses performed over different study areas reveal high quality percentage and precision metrics, exceeding 95%. Performance analysis over the SztaKi-Inria and Istanbul datasets shows that BDSV outperforms benchmark algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Section: The Sztaki-inria Datasetmentioning

confidence: 99%

Section: The Sztaki-inria Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autonomous Building Detection Using Edge Properties and Image Color Invariants

Ghandour

Jezzini

2018

Buildings

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“…The classification was conducted with the use of the pattern recognition methods on thin sections of five selected rocks. Grinias, Panagiotakis, and Tziritas (2016) presented a new method for automatic detection of building and road structures using satellite images based on novel image segmentation algorithm and pattern analysis techniques. Additionally, image processing and pattern recognition techniques have been used to map and monitor earthquakes, faulting, volcanic activity, landslides, flooding, wildfire and the damages associated with them (Joyce, Belliss, Samsonov, McNeill, & Glassey, 2009).…”

Section: Introductionmentioning

confidence: 99%

The contribution of pattern recognition techniques in geomorphology and geology: the case study of Tinos Island (Cyclades, Aegean, Greece)

Toulia

Kokinou

Panagiotakis

2017

European Journal of Remote Sensing

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Scope of the present work is to apply modern methods of pattern recognition concerning the automatic detection of geomorphological features (curvilinear lineaments and topographic highs), with emphasis on geological faults and to compare the results of the automatic detection with aerial photographs and further geological data collected in Tinos Island. The contribution of this work in the geosciences is multidisciplinary because (a) it proposes to geoscientists a new tool of geomorphological analysis prior to the fieldwork and especially at the stage where published work and already available data are collected, evaluated and reprocessed, (b) it increases the detection accuracy of the geological features both prior and after the fieldwork, (c) it decreases the time of fieldwork and consequently the cost of the entire geological research and (d) it can be applied in digital elevation data. The automatically detected linear-curvilinear lineaments and topographic highs agree in location, shape and orientation with the ground truth data (geological maps, aerial photographs and field measurements). Furthermore, the shape and the orientation of the most prominent topographic high (Tsiknias) seems to be related to the tectonic regime in the wide area of study corresponding to the ENE-WSW, WSW-ESE, N-S and E-W trending normal faults. ARTICLE HISTORY

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