A Framework for Supervised Image Classification with Incomplete Training Samples

Li, Wenkai; Liu, Desheng; Chen, Jin

doi:10.14358/pers.78.6.595

Cited by 20 publications

(21 citation statements)

References 24 publications

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“…The derived posteriori probabilities can be used as input in advanced spatial smoothing techniques [52,53] or for combining OCC outputs of several classes in one map [24,54]. With a posteriori probabilities it is also straightforward to consider different mis-classification costs for false positive and false negative classifications [55].…”

Section: Pamentioning

confidence: 99%

Can I Trust My One-Class Classification?

2014

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Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion.

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Section: Pamentioning

confidence: 99%

Can I Trust My One-Class Classification?

2014

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“…In this study, the Back Propagation (BP) neural network is used as the classifier. It has been proven that PUL is insensitive to outliers and parameters [71]. PUL is able to predict the probability of presence without negative data.…”

Section: Training and Predictingmentioning

confidence: 99%

“…This makes PUL an ideal method in regional urban extent mapping. More details of the principle and process of its deduction can be found in the paper [70][71][72].…”

Section: Training and Predictingmentioning

confidence: 99%

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

Wang

Wan

et al. 2017

Remote Sensing

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Abstract:The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer's accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user's accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.

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“…More details of the principle and process of its deduction can be found in [33]. It is worth noting that PUL is not a specific classifier but a general framework for classifier learning [22]. In this study, the back propagation (BP) neural network was used as the classifier.…”

Section: Pulmentioning

confidence: 99%

“…Unlike other one-class classification methods, it only requires the collection of presence and background samples, instead of both presence and absence samples, which further reduces the work involved in sample collection. Moreover, the background/unlabeled samples in PUL can be both positive and negative, which can help to improve the classification accuracy [22]. This has proved to be one of the best one-class methods for classifying land cover based on remote-sensing images [32].…”

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confidence: 99%

Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

Wan

Fang

et al. 2015

Remote Sensing

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Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy. OPEN ACCESSRemote Sens. 2015, 7 10144

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A Framework for Supervised Image Classification with Incomplete Training Samples

Cited by 20 publications

References 24 publications

Can I Trust My One-Class Classification?

Can I Trust My One-Class Classification?

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

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