Super-resolution land cover mapping with indicator geostatistics

Boucher, Alexandre; Kyriakidis, Phaedon C.

doi:10.1016/j.rse.2006.04.020

Cited by 133 publications

(83 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

mentioning

confidence: 99%

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

Chen

Heuvelink

et al. 2018

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

“…The IGSRM algorithm [33] performs indicator cokriging (ICK) [49] and sequential indicator simulation (SIS) [50] in two steps to produce SRM results. In the first step, also called the sub-pixel sharpening step [55], ICK is used to estimate the probability that an FR-pixel belongs to a particular class, given the coarse-resolution class proportions obtained from spectral unmixing and the prior model of the spatial structure at fine resolution.…”

Section: Igsrmmentioning

confidence: 99%

“…In the first step, also called the sub-pixel sharpening step [55], ICK is used to estimate the probability that an FR-pixel belongs to a particular class, given the coarse-resolution class proportions obtained from spectral unmixing and the prior model of the spatial structure at fine resolution. An indicator variogram model is used to quantify the prior spatial structure of each land cover class at fine resolution [33].…”

Section: Igsrmmentioning

confidence: 99%

See 1 more Smart Citation

Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method

Shi

Jin

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43-10.96% higher than that of the IGSRM method for different scale factors, and 1.09-3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42-4.92%, and 0.08-0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods.

show abstract

“…Geo-statistical methods (Boucher and Kyriakidis, 2006), extensions of the linear mixture model (Verhoeye and De Wulf, 1999), feed-forward back-propagation artificial neural network (Mertens et al, 2004) Spatial pixel swapping (Atkinson, 2005), spatial simulated annealing (Atkinson, 2004), Hopfield neural network (Tatem et al, 2001) Table 1. Two main categories of SRM techniques (adopted from Atkinson (2008)) According to the first law of geography, everything is related to everything else, but near things are more related than distant things (Tobler, 1970).…”

Section: Regressionmentioning

confidence: 99%

A Novel Approach to Super Resolution Mapping of Multispectral Imagery Based on Pixel Swapping Technique

Niroumand-Jadidi

Safdarinezhad

Sahebi

et al. 2012

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

View full text Add to dashboard Cite

ABSTRACT:Mixed pixels could be considered as a major source of uncertainty through classification process of satellite imagery. In this regard, the use of soft classifiers is often inevitable in order to increase the accuracy of land cover estimates. Although soft classifiers provide detailed information for each pixel, spatial arrangement of sub-pixels remains unknown. Super Resolution Mapping (SRM) has opened up a new horizon to produce finer spatial resolution maps using the outputs of soft classifiers. Wide variety of SRM algorithms has been developed. In this way, spatial optimisation techniques are the most applicable ones. However, random allocation of sub-pixels and iterative procedure of optimisation are the main limitations of current methods (e.g. Hopfield Neural Network, Simulated Annealing). This research attempts to provide an optimisation approach based on the pixel swapping technique in order to simplify the concept and to reduce the iteration procedure. In this paper, a brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed nonrandomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.

show abstract

Super-resolution land cover mapping with indicator geostatistics

Cited by 133 publications

References 24 publications

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

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

Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method

A Novel Approach to Super Resolution Mapping of Multispectral Imagery Based on Pixel Swapping Technique

Contact Info

Product

Resources

About