The Experts below are selected from a list of 36123 Experts worldwide ranked by ideXlab platform
Stephen V Stehman - One of the best experts on this subject based on the ideXlab platform.
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statistical rigor and practical utility in Thematic Map accuracy assessment
Photogrammetric Engineering and Remote Sensing, 2001Co-Authors: Stephen V StehmanAbstract:Quantifying Map accuracy provides important descriptive information to assess the utility of a Map for a specified application. This article focuses on site-specific, Thematic accuracy in which accuracy is defined by comparing the Map attribute and the actual attribute for a sample of areal units. Although statistical rigor and practical utility have been advocated as desirable features of Map accuracy assessment protocols, specific criteria defining these features have not been elucidated. Two criteria are proposed for statistical rigor: probability sampling and consistent estimation. Practical utility is synonymous with cost, and because cost is directly related to quality, decisions regarding practical utility may be evaluated in terms of their effect on quality. Four criteria are proposed to define quality: the precision of the accuracy estimates, the population to which sampling inference is justified, the assumptions needed to justify inference, and the accuracy of the reference data. The first step in planning a statistically rigorous, practical accuracy assessment is to construct an efficient, probability-sampling-based strategy permitting inference to the full Map population. Modifications of this strategy to enhance practical utility (i.e. reduce cost of the assessment) should be evaluated using the criteria defined for quality and statistical rigor.
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practical implications of design based sampling inference for Thematic Map accuracy assessment
Remote Sensing of Environment, 2000Co-Authors: Stephen V StehmanAbstract:Sampling inference is the process of generalizing from sample data to make statements or draw conclusions about a population. Design-based inference is the inferential framework commonly invoked when sampling techniques are used in Thematic Map accuracy assessment. The conceptual basis of design-based inference is described, followed by discussion of practical implications of design-based inference, including (1) the population to which the inferences apply, (2) estimation formulas and their justification, (3) interpretation of accuracy measures, (4) representation of variability, (5) effect of spatial correlation, and (6) role of probability sampling. Design-based inference is contrasted with model-based inference, another inferential framework frequently invoked in statistics.
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basic probability sampling designs for Thematic Map accuracy assessment
International Journal of Remote Sensing, 1999Co-Authors: Stephen V StehmanAbstract:Choosing a sampling design for assessing Thematic Map accuracy requires the strength of a sampling design to be matched to the objectives and resources available for the accuracy assessment. The criteria to consider when planning the sampling design are that the sample should: (1) satisfy probability sampling protocol; (2) be simple to implement and analyse; (3) result in low variance for the key estimates of the assessment; (4) permit adequate variance estimation; (5) be spatially well distributed; and (6) be cost effective. Several basic probability sampling designs useful for accuracy assessment are reviewed, and recommendations are provided to guide the selection of an appropriate design.
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design and analysis for Thematic Map accuracy assessment fundamental principles
Remote Sensing of Environment, 1998Co-Authors: Stephen V Stehman, Raymond L CzaplewskiAbstract:Abstract Before being used in scientific investigations and policy decisions, Thematic Maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to select the reference sample; 2) the response design used to obtain the reference land-cover classification for each sampling unit; and 3) the estimation and analysis procedures. We discuss options available for each of these components. A statistically rigorous assessment requires both a probability sampling design and statistically consistent estimators of accuracy parameters, along with a response design determined in accordance with features of the Mapping and classification process such as the land-cover classification scheme, minimum Mapping unit, and spatial scale of the Mapping.
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use of auxiliary data to improve the precision of estimators of Thematic Map accuracy
Remote Sensing of Environment, 1996Co-Authors: Stephen V StehmanAbstract:Abstract Collecting reference samples for accuracy assessment is expensive, so that statistical procedures that improve the precision (reduce variance) of estimators of Thematic Map accuracy while incurring little additional cost are desirable. Auxiliary data available from existing landcover Maps, aerial photographs, videography, or AVHRR, for example, can be used in combination with ground visits to enhance the precision of Map accuracy estimates. This precision gain is achieved by employing a regression estimator commonly used in survey sampling. Practically important gains in precision can be achieved with no additional complication in the sampling design and without additional field visits. The only additional cost incurred is that of obtaining the auxiliary data. The regression estimator can be used with simple random or systematic sampling if auxiliary information is available on the entire region represented by the land cover Map. Alternatively, a more practical design for accuracy assessment would be double sampling, which requires that the auxiliary information be obtained for only a subsample of the Map region. Appropriate estimators and standard errors are presented for using the regression estimator with either simple random or double sampling.
Peng Wang - One of the best experts on this subject based on the ideXlab platform.
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utilizing pansharpening technique to produce sub pixel resolution Thematic Map from coarse remote sensing image
Remote Sensing, 2018Co-Authors: Peng Wang, Liguo Wang, Henry LeungAbstract:Super-resolution Mapping (SRM) is a technique to obtain sub-pixel resolution Thematic Map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (Map) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher Mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset.
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producing fine resolution Thematic Map using interpolation then classification
International Geoscience and Remote Sensing Symposium, 2017Co-Authors: Peng Wang, Liguo WangAbstract:In this paper, a framework based on fine resolution Thematic Map, namely, interpolation then classification (ITC) is proposed. Firstly interpolation algorithm is applied in the original coarse hyperspectral imagery to derive a high-resolution imagery with generous prior information. Then fine resolution Thematic Map is derived from the high-resolution imagery by the available classification methods. Experiments on two real hyperspectral imagery showed that the proposed method produced higher accuracy result than interpolation-based soft-then-hard super-resolution Mapping (I-STHSRM).
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producing subpixel resolution Thematic Map from coarse imagery Map algorithm based super resolution recovery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Liguo Wang, Peng Wang, Chunhui ZhaoAbstract:Subpixel Mapping (SPM) of hyperspectral remote sensing imagery is a promising technique for deriving fine Mapping result by classification at fine spatial resolution. There is a type of algorithm for SPM, namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at subpixel level and then allocates classes for subpixel according to the soft attribute values. However, the fraction images derived from spectral unmixing are of less prior information of original hyperspectral remote sensing imagery and there are lots of errors in SPM result due to the limitation of spectral unmixing technology currently available. In this paper, a framework based on subpixel resolution Thematic Map, namely, super-resolution then classification (STC) is proposed to improve Mapping result. In the proposed framework, a maximum a posteriori (Map) model associated with the endmembers of interest (EOI), namely, T-Map-SR is applied to the original coarse imagery to derive a high-resolution imagery with generous prior information. Then fine Mapping result can be derived from the high-spatial resolution imagery by the available classification methods. Experiments show that the proposed framework can produce higher Mapping accuracy result and protect the classes of interest (COI).
Liguo Wang - One of the best experts on this subject based on the ideXlab platform.
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utilizing pansharpening technique to produce sub pixel resolution Thematic Map from coarse remote sensing image
Remote Sensing, 2018Co-Authors: Peng Wang, Liguo Wang, Henry LeungAbstract:Super-resolution Mapping (SRM) is a technique to obtain sub-pixel resolution Thematic Map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (Map) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher Mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset.
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producing fine resolution Thematic Map using interpolation then classification
International Geoscience and Remote Sensing Symposium, 2017Co-Authors: Peng Wang, Liguo WangAbstract:In this paper, a framework based on fine resolution Thematic Map, namely, interpolation then classification (ITC) is proposed. Firstly interpolation algorithm is applied in the original coarse hyperspectral imagery to derive a high-resolution imagery with generous prior information. Then fine resolution Thematic Map is derived from the high-resolution imagery by the available classification methods. Experiments on two real hyperspectral imagery showed that the proposed method produced higher accuracy result than interpolation-based soft-then-hard super-resolution Mapping (I-STHSRM).
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producing subpixel resolution Thematic Map from coarse imagery Map algorithm based super resolution recovery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016Co-Authors: Liguo Wang, Peng Wang, Chunhui ZhaoAbstract:Subpixel Mapping (SPM) of hyperspectral remote sensing imagery is a promising technique for deriving fine Mapping result by classification at fine spatial resolution. There is a type of algorithm for SPM, namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at subpixel level and then allocates classes for subpixel according to the soft attribute values. However, the fraction images derived from spectral unmixing are of less prior information of original hyperspectral remote sensing imagery and there are lots of errors in SPM result due to the limitation of spectral unmixing technology currently available. In this paper, a framework based on subpixel resolution Thematic Map, namely, super-resolution then classification (STC) is proposed to improve Mapping result. In the proposed framework, a maximum a posteriori (Map) model associated with the endmembers of interest (EOI), namely, T-Map-SR is applied to the original coarse imagery to derive a high-resolution imagery with generous prior information. Then fine Mapping result can be derived from the high-spatial resolution imagery by the available classification methods. Experiments show that the proposed framework can produce higher Mapping accuracy result and protect the classes of interest (COI).
Henry Leung - One of the best experts on this subject based on the ideXlab platform.
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utilizing pansharpening technique to produce sub pixel resolution Thematic Map from coarse remote sensing image
Remote Sensing, 2018Co-Authors: Peng Wang, Liguo Wang, Henry LeungAbstract:Super-resolution Mapping (SRM) is a technique to obtain sub-pixel resolution Thematic Map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (Map) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher Mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset.
Raymond L Czaplewski - One of the best experts on this subject based on the ideXlab platform.
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design and analysis for Thematic Map accuracy assessment fundamental principles
Remote Sensing of Environment, 1998Co-Authors: Stephen V Stehman, Raymond L CzaplewskiAbstract:Abstract Before being used in scientific investigations and policy decisions, Thematic Maps constructed from remotely sensed data should be subjected to a statistically rigorous accuracy assessment. The three basic components of an accuracy assessment are: 1) the sampling design used to select the reference sample; 2) the response design used to obtain the reference land-cover classification for each sampling unit; and 3) the estimation and analysis procedures. We discuss options available for each of these components. A statistically rigorous assessment requires both a probability sampling design and statistically consistent estimators of accuracy parameters, along with a response design determined in accordance with features of the Mapping and classification process such as the land-cover classification scheme, minimum Mapping unit, and spatial scale of the Mapping.
