Fusion of Scores in a Detection Context Based on Alpha Integration

Soriano, Antonio; Vergara, Luis; Bouziane, Ahmed; Salazar, Addisson

doi:10.1162/neco_a_00766

Cited by 48 publications

(35 citation statements)

References 27 publications

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“…In the future, new classifiers can be applied in the proposed method to improve the classification accuracy. Recently, a combination of results from different classifiers has been proposed [30] and our future study will investigate this approach, which may improve the results obtained in this paper.…”

Section: Discussionmentioning

confidence: 92%

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

et al. 2018

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Lithology classification is a crucial step in the prospecting process, and polarimetric synthetic aperture radar (Pol-SAR) imagery has been extensively used for it. However, despite significant improvements in both information content of Pol-SAR imagery and advanced classification approaches, lithology classification using Pol-SAR data may not provide satisfactory classification accuracy due to high similarity of certain classes. In this paper, a novel Pol-SAR lithology classification method based on a stacked sparse autoencoder (SSAE) is proposed. By using superpixel segmentation, new features can be extracted from dual-frequency Pol-SAR data, which can increase the class separability of the input data. Then, these features and the coherency matrices are incorporated into SSAE to classify the lithology. The classification performance is evaluated on an SIR-C dataset acquired over Xinjiang, China. The experimental result shows that this method is effective for lithology classification and can improve the overall accuracy up to 98.90%.

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Section: Discussionmentioning

confidence: 92%

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

et al. 2018

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“…As Westin [8] pointed out, a non-significant difference between AUCs for two methods does not imply an equivalence between these methods. A different point of view on AUC was provided by [12] which focused on the fusion of methods for different detectors to improve their individual performance. The authors investigated the maximization of AUC as one of the instruments for optimizing the fusion.…”

Section: Contemporary Methods For Roc Comparisonmentioning

confidence: 99%

Comparing Classifier's Performance Based on Confidence Interval of the ROC

Poměnková¹,

Malach²

2018

RADIOENGINEERING

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This paper proposes a new methodology for comparing two performance methods based on confidence interval for the ROC curve. The methods performed and compared are two algorithms for face recognition. The novelty of the paper is threefold: i) designing a methodology for the comparison of decision making algorithms via confidence intervals of ROC curves; ii) investigating how sample sizes influence the properties of the particular methods; iii) recommendations for a general comparison of decision making algorithms via confidence intervals of ROC curves. To support our conclusions we investigate and demonstrate several approaches for constructing parametric confidence intervals on real data. Thus, we present a non-traditional and reliable way of reporting pattern recognition results using ROC curves with confidence intervals.

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“…The aim of the proposed STI is to minimize T. In STI, we consider a weighted sum of the space and temperature elements of STI, because this information-fusion method has a simple physical meaning and is easy to implement. Of course, we can also use other more effective information-fusion techniques, such as multiobjective optimization [44], alpha integration [45], and so on.…”

Section: Implementation Of Stimentioning

confidence: 99%

Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Wang

Zhang

et al. 2019

Remote Sensing

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Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

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Fusion of Scores in a Detection Context Based on Alpha Integration

Cited by 48 publications

References 27 publications

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data

Comparing Classifier's Performance Based on Confidence Interval of the ROC

Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

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