Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Ling, Feng; Du, Youwei; Xiao, Fei; Han, Xue; Wu, Shengjun

doi:10.1080/01431160903252350

Cited by 104 publications

(69 citation statements)

References 46 publications

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“…Sub-pixel scale analyses have proved popular in ecological research, notably with the provision of products such as vegetation continuous fields (Hansen et al, 2002;Heiskanen, 2008). There are, however, also concerns with this type of analysis that can greatly limit its value (Foody and Doan, 2007;Ling et al, 2009;Ngigi et al, 2009). …”

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confidence: 99%

“…A variety of super-resolution analyses may be undertaken. In particular super-resolution restitution seeks to form a finer spatial resolution image which may then form the focus of interest (Farsiu et al, 2006;Ling et al, 2009) or super-resolution mapping which seeks to map at a sub-pixel scale (Foody et al, 2005). Super-resolution techniques have been shown able to increase the accuracy and realism of key features such as class boundaries (Foody et al, 2005; and provide useful information for ecological research.…”

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confidence: 99%

“…To address this problem some have sought to investigate ways of effectively increasing the spatial resolution of the imagery. This has often been achieved through use of a "sharpening" image following some form of image fusion analysis Cetin and Musaoglu, 2009;Jing and Cheng, 2009) or the adoption of a super-resolution mapping technique (Ling et al, 2009). The use of sharpening imagery has been popular with imagery acquired by sensors that operate at more than one spatial resolution (e.g.…”

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An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

113

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

113

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“…Recently, sub-pixel mapping (SPM) techniques, which predict the location of land cover classes within a coarse pixel (mixed pixel) [19,20], have also been proposed to generate a high-resolution classification map using fractional abundance images. Various methods based on linear optimization technique [21], pixel/sub-pixel spatial attraction model [22], pixel swapping algorithm [23], maximum a posteriori (MAP) model [24,25], Markov random field (MRF) [26,27], artificial neural network (ANN) [28][29][30], simulated annealing [31], total variant model [32], support vector regression [33], and collaborative representation [34] are proposed. In general, sub-pixel based analysis only overcomes the limitation in spatial-resolution for certain applications, e.g., classification and target detection.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

et al. 2017

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Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.

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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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Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Cited by 104 publications

References 46 publications

An overview of recent remote sensing and GIS based research in ecological informatics

An overview of recent remote sensing and GIS based research in ecological informatics

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

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

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