The impact of image and class structure upon sub-pixel mapping accuracy using the pixel-swapping algorithm

Luciani, Peter; Chen, Dongmei

doi:10.1080/19475683.2011.558022

Cited by 14 publications

(11 citation statements)

References 18 publications

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“…If the intrinsic scale of variation is not smaller than the sampling scale imposed by the image pixels, land cover is spatially dependent both within and between pixels [6]- [9]. This finding has been proven tenable in most cases.…”

Section: Introductionmentioning

confidence: 85%

“…This finding has been proven tenable in most cases. Based on this principle, many algorithms have been applied to deal with sub-pixel allocation, such as attraction model [10], pixel swapping method [9], [11], [12], SPM_HIIPD method [6], and so on. Sub-pixel mapping can thus be formulated as an optimization problem that aims to maximize spatial dependence, whereas preserving the area proportion of each class.…”

Section: Introductionmentioning

confidence: 99%

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Improved Sub-Pixel Mapping Method Coupling Spatial Dependence With Directivity and Connectivity

Liu

et al. 2014

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

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Accurate land cover mapping by using coarse resolution imageries has been an attractive research topic. Sub-pixel mapping has been proven efficient for allocating sub-pixels within a mixed pixel. The most likely distribution can be determined on the condition of maximized spatial dependence. However, linear land cover like roads and rivers cannot be predicted efficiently because of weaker spatial dependence between and within mixed pixels. To obtain more accurate classification at the sub-pixel scale, an improved sub-pixel mapping method by combining spatial dependence with directivity and connectivity of linear land cover was proposed. Central line of linear land cover was extracted from fraction images to provide site-specific information. Discriminated allocation targets were accordingly designed: both connectivity and directivity were considered as important auxiliary information for allocating linear land cover, whereas only maximized spatial dependence is required for other classes. Then, simulated annealing arithmetic (SAA) was applied to optimize sub-pixel allocation. The method was evaluated visually and quantitatively with the accuracy indices. Compared with the model that considers only spatial dependence, SPM HIIPD method, attraction model and hard classifier (MLC), the improved method can increase classification accuracy at the sub-pixel scale with both simulated imageries and partial SPOT remotely sensed imagery.Index Terms-Directivity and connectivity, simulated annealing arithmetic (SAA), spatial dependence, sub-pixel mapping.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Improved Sub-Pixel Mapping Method Coupling Spatial Dependence With Directivity and Connectivity

Liu

et al. 2014

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

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“…Most previous works on SRM are related to the first approach, e.g. those based on sub-pixel swapping (Luciani and Chen 2011;Thornton, Atkinson, and Holland 2006;Xu and Huang 2014), multiple endmember spectral mixture analysis (Powell et al 2007), geostatistics (Boucher and Kyriakidis 2006;Atkinson, Pardo-Iguzquiza, and Chica-Olmo 2008;Wang, Shi, and Wang 2014), and spatial attraction which was first introduced by Mertens, De Baets, et al (2004). The key issue of the last method is to find the neighbouring sub-pixels or pixels which should attract the target sub-pixel.…”

Section: Introductionmentioning

confidence: 98%

Fully spatially adaptive smoothing parameter estimation for Markov random field super-resolution mapping of remotely sensed images

Aghighi

Trinder

Lim

et al. 2015

International Journal of Remote Sensing

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This article presents a fully spatially adaptive Markov random field (MRF)-based super-resolution mapping (SRM) technique to produce land-cover maps at a finer spatial resolution than the original coarse-resolution image. MRF combines the spectral and spatial energies; hence, an MRF-SRM technique requires a smoothing parameter to manage the contributions of these energies. The main aim of this article is to introduce a new method called fully spatially adaptive MRF-SRM to automatically determine the smoothing parameter, overcoming limitations of the previously proposed approaches. This method estimates the number of endmembers in each image and uses them to assess the proportions of classes within each coarse pixel by a linear spectral unmixing method. Then, the real pixel intensity vectors and the local properties of each coarse pixel are used to compute the local spectral energy change matrix and the local spatial energy change matrix for each coarse pixel. Each pair of matrices represents all possible situations in spatial and spectral energy change for each coarse pixel and can be used to examine the balance between spatial and spectral energies, and hence to estimate a smoothing parameter for each coarse pixel. Thus, the estimated smoothing parameter is fully spatially adaptive with respect to real pixel spectral vectors and their local properties. The performance of this method is evaluated using two synthetic images and an EO1-ALI (The Advanced Land Imager instrument on Earth Observing-1 satellite) multispectral remotely sensed image. Our experiments show that the proposed method outperforms the state-of-the-art techniques.

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“…PS method was firstly raised and tested on simulated imagery by Atkinson (2001) and then developed by other researchers such as Thornton et al (2006), Luciani andChen (2011) andYuan-Fong et al (2012). Firstly, the binary classification image at sub-pixel level is generated by random allocation according to the fractions of classes.…”

Section: Pixel Swapping (Ps)mentioning

confidence: 99%

A Comparison of Sub-Pixel Mapping Methods for Coastal Areas

Liu¹,

Trinder²,

Turner³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Sub-pixel Mapping, Coastal Areas, Soft Classification, Multispectral Images, Supervised Classification, Waterline ABSTRACT:This paper presents the comparisons of three soft classification methods and three sub-pixel mapping methods for the classification of coastal areas at sub-pixel level. Specifically, SPOT-7 multispectral images covering the coastal area of Perth are selected as the experiment dataset. For the soft classification, linear spectral unmixing model, supervised fully-fuzzy classification method and the support vector machine are applied to generate the fraction map. Then for the sub-pixel mapping, the sub-pixel/pixel attraction model, pixel swapping and wavelets method are compared. Besides, the influence of the correct fraction constraint is explored. Moreover, a post-processing step is implemented according to the known spatial knowledge of coastal areas. The accuracy assessment of the fraction values indicates that support vector machine generates the most accurate fraction result. For sub-pixel mapping, wavelets method outperforms the other two methods with overall classification accuracy of 91.79% and Kappa coefficient of 0.875 after the post-processing step and it also performs best for waterline extraction with mean distance of 0.71m to the reference waterline. In this experiment, the use of correct fraction constraint decreases the classification accuracy of sub-pixel mapping methods and waterline extraction. Finally, the post-processing step improves the accuracy of sub-pixel mapping methods, especially for those with correct coefficient constraint. The most significant improvement of overall accuracy is as much as 4% for the subpixel/pixel attraction model with correct coefficient constraint.

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The impact of image and class structure upon sub-pixel mapping accuracy using the pixel-swapping algorithm

Cited by 14 publications

References 18 publications

Improved Sub-Pixel Mapping Method Coupling Spatial Dependence With Directivity and Connectivity

Improved Sub-Pixel Mapping Method Coupling Spatial Dependence With Directivity and Connectivity

Fully spatially adaptive smoothing parameter estimation for Markov random field super-resolution mapping of remotely sensed images

A Comparison of Sub-Pixel Mapping Methods for Coastal Areas

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