Latent Class Modeling for Site- and Non-Site-Specific Classification Accuracy Assessment Without Ground Data

2012 ◽  
Vol 50 (7) ◽  
pp. 2827-2838 ◽  
Author(s):  
Giles M. Foody
Keyword(s):  
Classification Accuracy ◽  
Latent Class ◽  
Accuracy Assessment ◽  
Site Specific ◽  
Latent Class Modeling

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals Coupling Uncertainties with Accuracy Assessment in Object-based Slum Detections, Case Study: Jakarta, Indonesia

2017 ◽  
Author(s):  
Jati Pratomo ◽  
Monika Kuffer ◽  
Javier Martinez ◽  
Divyani Kohli
Keyword(s):  
Land Tenure ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
False Negative ◽  
National Policy ◽  
True Positive ◽  
True Negative ◽  
Object Based ◽  
The Impact

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

scholarly journals Multilevel latent class modeling to segment the microfinance market

2019 ◽  
Vol 37 (5) ◽  
pp. 1103-1118
Author(s):  
Lucas Lopes Ferreira Souza ◽  
Francesca Bassi ◽  
Ana Augusta Ferreira de Freitas
Keyword(s):  
Market Segmentation ◽  
Financial Services ◽  
Latent Class ◽  
Latent Class Models ◽  
Microfinance Institutions ◽  
Content Type ◽  
Business Results ◽  
Latent Class Modeling ◽  
Class Models ◽  
Do So

Purpose Microfinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existence of diverse types of microfinance organizations and client profiles. Microfinance institutions must primarily pay more attention to the client, and to the mechanism through which financial services are delivered. The purpose of this paper is to identify the profiles of microfinance customers and the features of their operations. Design/methodology/approach In this paper, multilevel latent class models were estimated to reveal clusters of operations and classes of clients. Findings The results show that there are six clusters of operations and four classes of clients in the market, each with distinct profiles and needs. Different strategies are recommended for each cluster and class. Originality/value Numerous studies have focused on the importance of getting to know the clients of microfinance programs, but none as yet have used market segmentation as a way to do so. The goal is to generate better strategies to help clients improve their business results. Applying market segmentation to the microfinance market may point to different products for different groups of clients, taking the real needs of each of them into account.

scholarly journals Do Interpersonal Conflict, Aggression and Bullying at the Workplace Overlap? A Latent Class Modeling Approach

2018 ◽  
Vol 9 ◽  
Author(s):  
Guy Notelaers ◽  
Beatrice Van der Heijden ◽  
Hannes Guenter ◽  
Morten Birkeland Nielsen ◽  
Ståle Valvetne Einarsen
Keyword(s):  
Latent Class ◽  
Interpersonal Conflict ◽  
Modeling Approach ◽  
Latent Class Modeling

scholarly journals The potential of Latent Class Analysis in diagnostic test validation for canine Leishmania infantum infection

1999 ◽  
Vol 123 (3) ◽  
pp. 499-506 ◽  
Author(s):  
M. BOELAERT ◽  
K. AOUN ◽  
J. LIINEV ◽  
E. GOETGHEBEUR ◽  
P. VAN DER STUYFT
Keyword(s):  
Latent Class Analysis ◽  
Leishmania Infantum ◽  
Latent Class ◽  
Accuracy Assessment ◽  
Clinical Status ◽  
Test Validation ◽  
Class Analysis ◽  
Serological Tests ◽  
Disease Epidemiology ◽  
Standard Problem

Accuracy assessment of diagnostic tests may be seriously biased if an imperfect reference test is used such as parasitology in the diagnosis of visceral leishmaniasis. We compared classical validity analysis of serological tests for Leishmania infantum with Latent Class Analysis (LCA), to assess whether it circumvented the gold standard problem. Clinical status, three serological tests (IFAT, ELISA and DAT) and parasitological data were recorded for 151 dogs captured in an endemic area. Sensitivity and specificity estimates from the 2×2 contingency tables were broadly corroborated by LCA, but the latter method provided more precise estimates that were robust for the different fitted models. It furthermore yielded a higher prevalence of infection and indicated that parasitology was only 55% sensitive. LCA seems a promising technique for test validation, but caution is required when applying it to sparse data sets. The feasibility and applicability of LCA in infectious disease epidemiology is discussed.

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