River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines

Güneralp, İnci; Filippi, Anthony M.; Hales, Billy

doi:10.1080/15481603.2013.778560

Cited by 28 publications

(34 citation statements)

References 47 publications

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“…Machine learning is a novel approach used in various remote sensing applications, including land cover/land use classification [46][47][48][49][50][51][52], change detection [53,54], geological mapping [55], vegetation mapping [56][57][58][59], hydrological studies [60][61][62] and atmospheric studies [63,64]. In this study, two rule-based machine learning approaches-decision tree (DT) and random forest (RF)-were used for the classification of open water, sea ice and melt pond from the TerraSAR-X dual-polarization [65], was used to carry out the DT-based classification.…”

Section: Machine Learning Approaches For Melt Pond Retrievalmentioning

confidence: 99%

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches-decision trees (DT) and random forest (RF)-in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15ˆ15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

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Section: Machine Learning Approaches For Melt Pond Retrievalmentioning

confidence: 99%

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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“…Selection of a kernel function and its parameterization are crucial for successful implementation of SVM [54,55]. There are many kernel functions available, and radial basis functions (RBF) have been widely used for remote sensing applications [41,56,57]. In this study, the library for SVM (LIBSVM) software package [58] with a RBF kernel was adopted and the parameters of the kernel were optimized through a grid-search algorithm in LIBSVM.…”

Section: Machine Learning Approaches For CI Detectionmentioning

confidence: 99%

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Han

Lee

et al. 2015

Remote Sensing

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As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches-decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15-75 min before the lightning occurrence were used as input variables for CI OPEN ACCESSRemote Sens. 2015, 7 9185 detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30-45 min in advance using COMS MI data.

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“…Second, choosing the feasible classification method is important for improving the final mapping accuracy. The SVM classification method is a useful tool for processing multispectral imagery (Huang, Davis, and Townshend 2002;Foody and Mathur 2004;2 L. Xun and L. Wang Mountrakis, Im, and Ogole 2011;Mondal et al 2012;Chu et al 2012;Li, Im, and Beier 2013;Güneralp, Filippi, and Hales 2013). Recently, this technique has been widely applied to mapping salt cedar or other vegetation.…”

Section: Introductionmentioning

confidence: 99%

An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery

Wang

2015

GIScience & Remote Sensing

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Salt cedar, a predominant arid type of vegetation in Western China, provides invaluable ecosystem services and is important for economic sustainability. Most previous attempts to map salt cedar have been focused at the pixel level and not at the object level. A particular problem for object-based classification is determining the optimal scale for the segmentation. In this study, we proposed a new object-based image analysis method for classifying high resolution satellite image with support vector machine (SVM). Specifically, we set forth three objectives: (1) to choose the optimal multiscale parameters for different cover types with the aid of the Bhattacharyya Distance index; (2) to extract the class specific features for different classes to feed into the SVM classification procedure; and (3) to compare three different classification methods: the integration of object-SVM, SVM at the pixel level and nearest-neighbor at the object level. The result of the case study demonstrated that the multiscale object-SVM method, which employed spectral, texture and shadow features, produced the best overall accuracy (91.6%).

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River-flow boundary delineation from digital aerial photography and ancillary images using Support Vector Machines

Cited by 28 publications

References 47 publications

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery

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