Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

doi:10.1016/j.isprsjprs.2017.11.014

Cited by 65 publications

(32 citation statements)

References 66 publications

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“…Membership functions assign fuzzy values to a predefined feature space for each object class using a particular membership function (larger than, smaller than, singleton, Gaussian, about range, and full range) [29,36,46]. Features with high membership values (depending on the test conditions) are selected for classification [33,52,53]. The classification operator is another important factor that affects classification accuracy since it determines how several rules are combined.…”

Section: Membership Function Methodsmentioning

confidence: 99%

“…From its onset, OBIA has often been associated with fuzzy methods, where objects are assigned to a particular class based on fuzzy relations and rules. Many studies illustrate how to assign particular objects to classes based on obtained fuzzy membership values for each object class and fuzzy rules combining several such rules [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

et al. 2019

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Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

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Section: Membership Function Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

et al. 2019

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“…In general, the existing road extraction methods comprise three categories: image-based [10], LiDAR-based [11], and trajectory-based methods [12]. Compared to trajectory-based methods, LiDAR-based and image-based methods are probably more precise, but are costly [13].…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data

Zhang

Huang

et al. 2020

IJGI

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With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.

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“…GeoSpatial Conference 2019 -Joint Conferences of SMPR and GI Research, 12-14 October 2019, Karaj, Iran objects characteristics (Maboudi et al, 2018) . This fuzzy object based road extraction approach exploits an integration of fuzzy reasoning in a bio-inspired swarm intelligence algorithm, namely object based ant colony optimization (ACO), for extraction of road network.…”

Section: Road Extractionmentioning

confidence: 99%

“…Using the above mentioned criteria the geometric quality of the extracted road network can be evaluated. However, for a thorough evaluation of the quality of a road network, geometric criteria alone are not enough because of their inability to reflect the possible imperfections in network properties (topology) of the results (Wegner et al, 2015, Maboudi et al, 2018 .…”

Section: Geometric Criteriamentioning

confidence: 99%

Impact of Gap Filling on Quality of Road Networks

Maboudi

Amini

Gerke

2019

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

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High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications

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Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

Cited by 65 publications

References 66 publications

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data

Impact of Gap Filling on Quality of Road Networks

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