A Weighted Minimum Distance Classifier Based on Relative Offset

Wang, Yuxing; Jiang, Yulian

doi:10.1109/icccbda.2019.8725734

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…For the land cover classification process, this research used the machine learning (ML) Mahalanobis distance (MD) method, which included identifying plastic waste as the primary target for both the Pleiades and UAV photogrammetry images. The MD method is a type of guided classification widely used for classifying remote sensing images 41 . The Mahalanobis distance method in the classification process was implemented by measuring the distance between two data samples in a multivariate space 42 .…”

Section: Data Used In This Studymentioning

confidence: 99%

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery

Sakti

Sembiring

Rohayani

et al. 2023

Sci Rep

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Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring plastic waste in river areas. This study aims to identify illegal dumping in a river area using the adjusted plastic index (API) and Sentinel-2 satellite imagery data. Rancamanyar River has been selected as the research area; it is one of the tributaries of Citarum Indonesia and is an open lotic-simple form, oxbow lake type river. Our study is the first attempt to construct an API and random forest machine learning using Sentinel-2 to identify the illegal dumping of plastic waste. The algorithm development integrated the plastic index algorithm with the normalized difference vegetation index (NDVI) and normalized buildup indices. For the validation process, the results of plastic waste image classification based on Pleiades satellite imagery and Unmanned Aerial Vehicle (UAV) photogrammetry was used. The validation results show that the API succeeded in improving the accuracy of identifying plastic waste, which gave a better correlation in the r-value and p-value by + 0.287014 and + 3.76 × 10−26 with Pleiades, and + 0.143131 and + 3.17 × 10−10 with UAV.

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Section: Data Used In This Studymentioning

confidence: 99%

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery

Sakti

Sembiring

Rohayani

et al. 2023

Sci Rep

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“…A minimum distance classifier is usually applied due to its flexibility and efficacy; however, it does have the downside of poor classification precision. The weighted minimum distance classifier based on relative offset was proposed by Wang and Jiang (2019) and the efficiency of the minimum distance classifier was boosted.…”

Section: Minimum Distance Classificationmentioning

confidence: 99%

A review of machine learning in processing remote sensing data for mineral exploration

Shirmard,

Farahbakhsh,

Muller

et al. 2021

Preprint

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As a primary step in mineral exploration, a variety of features are mapped such as lithological units, alteration types, structures, and minerals. These features are extracted to aid decision-making in targeting ore deposits. Different types of remote sensing data including satellite optical and radar, airborne, and drone-based data make it possible to overcome problems associated with mapping these important parameters on the field. The rapid increase in the volume of remote sensing data obtained from different platforms has allowed scientists to develop advanced, innovative, and powerful data processing methodologies. Machine learning methods can help in processing a wide range of remote sensing data and in determining the relationship between the reflectance continuum and features of interest. Moreover, these methods are robust in processing spectral and ground truth measurements

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“…The Euclidian distance between two pixels spectral feature vectors in n dimensional space is derived from the spectral angle measure as [21,27]:…”

Section: (C) Spectral Angle Measurementioning

confidence: 99%

“…, S jn ) T is the reference (endmember or library) spectrum in the n dimensional feature space respectively. The main difference between SAM (and the spectral correlation) and Euclidian distance measure is that the spectral angle is invariant with brightness whereas the Euclidian distance considers the brightness difference between the two vectors [21,27]. (E) Spectral Information Divergence This is also a whole-pixel classification technique based on information theory, mostly abbreviated as SID.…”

Section: (C) Spectral Angle Measurementioning

confidence: 99%

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

2020

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Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

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A Weighted Minimum Distance Classifier Based on Relative Offset

Cited by 5 publications

References 13 publications

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery

A review of machine learning in processing remote sensing data for mineral exploration

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

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