Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices

Li, Xiaodong; Du, Youwei; Ling, Feng; Wu, Shengjun; Feng, Qi

doi:10.1016/j.ecolind.2010.12.016

Cited by 39 publications

(12 citation statements)

References 39 publications

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“…D(X, F ) represents the closeness of X to input F and is used to denote the minimum difference between the fraction values of the result and input fraction values. D(X, F ) is based on the area constraints in (2) and is formulated as…”

Section: Fraction Errorsmentioning

confidence: 99%

Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

Zhang

Ling

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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Super-resolution mapping (SRM) is a method for generating a fine-resolution land cover map from coarse-resolution fraction images. Example-regression-based SRM algorithms can estimate a fine-resolution land cover map with detailed spatial information by learning land cover spatial patterns from available land cover maps. Existing example-regression-based SRM algorithms are sensitive to fraction errors, and the results often include many linear artifacts and speckles. To overcome these shortcomings, this study proposes an improved example-regression-based SRM algorithm. The objective function of the proposed SRM algorithm comprises three terms. The first term is used to minimize the difference between the fraction values of the estimated fineresolution land cover map and the input fraction values. The second term is used to maximize the class membership possibility values of the fine pixels in the result. The final term is used to make the result locally smooth. The proposed SRM algorithm is compared with several popular SRM algorithms using both synthetic and real fraction images. Experimental results indicate that the proposed SRM algorithm can produce results with less speckles and linear artifacts, more spatial details, smoother boundaries, and higher accuracies than the SRM results used for comparison.Index Terms-Example based, subpixel mapping (SPM), superresolution mapping (SRM), support vector regression (SVR).

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Section: Fraction Errorsmentioning

confidence: 99%

Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

Zhang

Ling

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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“…These approaches have achieved relatively satisfactory performance in several applications, such as waterline mapping [29], urban tree identification [13], enhancement of the landscape pattern index [30], land-cover change detection [31], urban building extraction [32], lake area estimation [33], and floodplain inundation mapping [34].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard Super-Resolution Mapping With Multiple Shifted Images

Chen

Heuvelink

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multiple shifted images (MSIs) have been widely applied to many super-resolution mapping (SRM) approaches to improve the accuracy of fine-scale land-cover maps. Most SRM methods with MSIs involve two processes: subpixel sharpening and class allocation. Complementary information from the MSIs has been successfully adopted to produce soft attribute values of subpixels during the subpixel sharpening process. Such information, however, is not used in the second process of class allocation. In this paper, a new class-allocation algorithm, named "hybrid constraints of pure and mixed pixels" (HCPMP), is proposed to allocate land-cover classes to subpixels using MSIs. HCPMP first determines the classes of subpixels that overlap with the pure pixels of auxiliary images in MSIs, after which the remaining subpixels are classified using information derived from the mixed pixels of the base image in MSIs. An artificial image and two remote sensing images were used to evaluate the performance of the proposed HCPMP algorithm. The experimental results demonstrate that HCPMP successfully applied MSIs to produce SRM maps that are visually closer to the reference images and that have greater accuracy than five existing class-allocation algorithms. Especially, it can produce more accurate SRM maps for high-resolution land-cover classes than low-resolution cases. The algorithm takes slightly less runtime than class allocation using linear optimization techniques. Hence, HCPMP provides a valuable new solution for class allocation in SRM using auxiliary data from MSIs.Index Terms-Hybrid constraints, multiple shifted images (MSIs), remotely sensed imagery, super-resolution mapping (SRM).

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“…To solve this problem, the subpixel mapping technique was proposed to determine the subpixel location of each class or endmember based on the fractional abundances [10][11][12][13][14][15]. In recent years, subpixel mapping has been applied in many practical applications, including land-cover change detection [16], waterline mapping [17], and landscape pattern indices calculation [18]. …”

Section: Introductionmentioning

confidence: 99%

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

Feng

Zhong

Wu³

et al. 2016

Remote Sensing

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Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations.

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Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices

Cited by 39 publications

References 39 publications

Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

Improvement of the Example-Regression-Based Super-Resolution Land Cover Mapping Algorithm

Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard Super-Resolution Mapping With Multiple Shifted Images

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

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