Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

Ge, Yong; Chen, Yuehong; Li, Sanping; Jiang, Yu

doi:10.1080/01431161.2014.882034

Cited by 38 publications

(38 citation statements)

References 13 publications

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“…Over the past decades, many SRM methods have been proposed. These methods involve the pixel swapping algorithm [7,13], Hopfield neural networks [14][15][16], subpixel/pixel spatial attraction models [17][18][19], Markov random fields [20][21][22][23], the geometric methods [24,25], geostatistical methods [26][27][28], artificial intelligence-based algorithms [29][30][31][32][33] and interpolation-based methods [34][35][36]. These methods have obtained acceptable performances in various applications, such as urban tree identification [37], urban building extraction [38], floodplain inundation mapping [39,40] and land use mapping [41].…”

mentioning

confidence: 99%

Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

Chen

Heuvelink

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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confidence: 99%

Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

Chen

Heuvelink

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…It is considered an efficient method for predicting the spatial distribution of LULC classes at subpixel scale. VBSPM was proposed by Ge et al (2014) to improve the geometric SPM method [28]. Similar to SAMSPM, VBSPM is based on spatial dependence.…”

Section: Experiments and Methodsmentioning

confidence: 99%

“…A ray-crossing algorithm is then used to determine the LULC class of each subpixel within each vectorial boundary [27]:…”

Section: Vbspmmentioning

confidence: 99%

Designing an Experiment to Investigate Subpixel Mapping as an Alternative Method to Obtain Land Use/Land Cover Maps

Jiang

Chen

et al. 2016

Remote Sensing

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Abstract:Various subpixel mapping (SPM) methods have been proposed as downscaling techniques to reduce uncertainty in classifying mixed pixels. Such methods can provide category maps of a higher spatial resolution than the original input images. The aim of this study was to explore and validate the potential of SPM as an alternative method for obtaining land use/land cover (LULC) maps of regions where high-spatial-resolution LULC maps are unavailable. An experimental design was proposed to evaluate the feasibility of SPM for providing the alternative LULC maps. A case study was implemented in the Jingjinji region of China. SPM results for spatial resolutions of 500-100 m were derived from a single 1-km synthetic fraction image using two representative SPM methods. The 1-km synthetic fraction image was assumed to be error free. Accuracy assessment and analysis showed that overall accuracies of the SPM results were reduced from about 85% to 75% with increasing spatial resolution, and that producer's accuracies varied considerably from about 62% to 93%. SPM performed best when handling areal features in comparison with linear and point features. The highest accuracies were achieved for areas with the lowest complexity. The study concluded that the results from SPM could provide an alternative LULC data source with acceptable accuracy, especially in areas with low complexity and with a large proportion of areal features.

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“…Sub-pixel mapping can be considered to be a post-processing stage of soft classification, in which the fraction images produced by soft classification are used as input to estimate a hard land cover map with fine spatial resolution [18]. A variety of sub-pixel mapping algorithms have been proposed, such as Hopfield neural networks [19][20][21], pixel-swapping algorithm [22], Markov random field [23], spatial attraction algorithms [24][25][26][27][28], vectorial boundary based algorithms [29,30], computational intelligence algorithms [31][32][33], and spatial regularization algorithm [34][35][36][37]. Sub-pixel mapping has been successfully used in many applications, such as the mapping urban trees [38], lakes [39], burned area [40] as well as in the refinement of ground control point location [41] and in the calculation of landscape pattern indices [42].…”

Section: Introductionmentioning

confidence: 99%

Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery

Ling

Foody

et al. 2016

Remote Sensing

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Abstract:Remotely sensed imagery is an attractive source of information for mapping and monitoring land cover. Fine spatial resolution imagery is typically acquired infrequently, but fine temporal resolution systems commonly provide coarse spatial resolution imagery. Sub-pixel land cover change mapping is a method that aims to use the advantages of these multiple spatial and temporal resolution sensing systems. This method produces fine spatial and temporal resolution land cover maps, by updating fine spatial resolution land cover maps using coarse spatial resolution remote sensing imagery. A critical issue for sub-pixel land cover change mapping is downscaling coarse spatial resolution fraction maps estimated by soft classification to a fine spatial resolution land cover map. The relationship between a historic fine spatial resolution map and a contemporary fine spatial resolution map to be estimated at a more recent date plays an important role in the downscaling procedure. A change strategy based on the assumption that the change for each land cover class in a coarse spatial resolution pixel is unidirectional was shown to be a promising means to describe this relationship. This paper aims to assess this change strategy by analyzing the factors that affect the accuracy of the change strategy, using six subsets of the National Land Cover Database (NLCD) of USA. The results show that the spatial resolution of coarse pixels, the time interval of the previous fine resolution land cover map and the current coarse spatial resolution images, and the thematic resolution of the used land cover class scheme have considerable influence on the accuracy of the change strategy. The accuracy of the change strategy decreases with the coarsening of spatial resolution, an increase of time interval, and an increase of thematic resolution. The results also indicate that, when the historic land cover map has a 30 m resolution, like the NLCD, the average accuracy of the change strategy is still as high as 92% when the coarse spatial resolution data used had a resolution of~1000 m, confirming the effectiveness of the change strategy used in sub-pixel land cover change mapping for use with popular remote sensing systems.

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Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

Cited by 38 publications

References 13 publications

Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery

Designing an Experiment to Investigate Subpixel Mapping as an Alternative Method to Obtain Land Use/Land Cover Maps

Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery

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