The integrated use of optical and InSAR data for urban land‐cover mapping

Amarsaikhan, D.; Ganzorig, M.; Ache, Peter; Blotevogel, Hans Heinrich

doi:10.1080/01431160600784267

Cited by 39 publications

(22 citation statements)

References 9 publications

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“…While the complementarity of data from both optical and radar sensors for the characterization of LUCC has been put to use in many very recent studies, e.g., [42][43][44][45][46][47], the development of adequate data fusion techniques is an important ongoing field of research [48]. In general, fusion refers to a formal concept for combining data from different sources [49,50], with the aim of generating information of "greater quality" than the individual input datasets.…”

Section: Land Covermentioning

confidence: 99%

“…However, only 5 of the studies with multitemporal data used this information for change detection; for the vast majority of the 27 multi-temporal studies, information was used to assist in creating a mono-temporal output (Table 6). Studies that also perform change detection 5 (6,14,17,42,43) Most studies (37 of 50 studies) integrated optical and radar before classification or modelling, thus letting all information from the input data influence the results, while 16 studies performed a post-classification or post-modelling fusion (Table 7). Table 7.…”

Section: Specifications Of Analyses In Studies Addressing Land Usementioning

confidence: 99%

“…Pre-classification or -modelling: fusion of input data 37 (1,3,4,7,10,11,13,14,15,16,17,18,21,22,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,41,44,45,46,47,48,50) Post-classification or -modelling: fusion of derived information 16 (2,5,6,8,9,12,19,20,23,28,34,37,42,43,44,…”

Section: Integration Step Number Of Studies Study Idsmentioning

confidence: 99%

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A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

et al. 2016

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Abstract:The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300-3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales.

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Section: Land Covermentioning

confidence: 99%

Section: Specifications Of Analyses In Studies Addressing Land Usementioning

confidence: 99%

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A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

et al. 2016

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“…Unlike single-source data, data sets from multiple sources have proved to offer better potential for discriminating between different land cover types. Many authors have assessed the potential of multisource images for the classification of different land cover classes [10][11][12][13][14][15]. In RS applications, the most widely used multisource classification techniques are statistical methods, Dempster-Shafer theory of evidence, neural networks, decision tree classifier, and knowledge-based methods [10,16,17].…”

Section: Introductionmentioning

confidence: 99%

Advanced Classification of Lands at TM and Envisat Images of Mongolia

Vandansambuu

Amarsaikhan²,

Bat-Erdene³

et al. 2013

ARS

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The aim of this study is to fuse high resolution optical and microwave images and classify urban land cover types using a refined Mahalanobis distance classifier. For the data fusion, multiplicative method, Brovey transform, intensity-huesaturation method and principal component analysis are used and the results are compared. The refined method uses spatial thresholds defined from local knowledge and the bands defined from multiple sources. The result of the refined Mahalanobis distance method is compared with the result of a standard technique and it demonstrates a higher accuracy. Overall, the research indicates that the combined use of optical and microwave images can notably improve the interpretation and classification of land cover types and the refined Mahalanobis classification is a powerful tool to increase classification accuracy.

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“…This can be achieved at any one of the three different processing levels of the image information: pixel (lowlevel), feature (medium-level) and classifier (high-level) fusion approaches (Yitayew, 2012 Many studies have been published to combine SAR and optical data for a number of applications (Amarsaikhan, 2007, Ricchetti, 2001, Venkataraman, 2004, Hong, 2014. To mention some of the applications, two SAR datasets from ERS-1/2 (C-band) and JERS-1 (L-band) are fused with a multi-spectral dataset from the SPOT satellite for the purpose of urban land cover classification (Amarsaikhan, 2007). In (Ricchetti, 2001) a study was conducted to fuse images from ERS-1 satellite and Landsat thematic mapper sensors for geological study purposes.…”

Section: Introductionmentioning

confidence: 99%

Classifier Fusion of High-Resolution Optical and Synthetic Aperture Radar (Sar) Satellite Imagery for Classification in Urban Area

Alipour-Fard

Hasanlou

Arefi

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This study concerned with fusion of synthetic aperture radar and optical satellite imagery. Due to the difference in the underlying sensor technology, data from synthetic aperture radar (SAR) and optical sensors refer to different properties of the observed scene and it is believed that when they are fused together, they complement each other to improve the performance of a particular application. In this paper, two category of features are generate and six classifier fusion operators implemented and evaluated. Implementation results show significant improvement in the classification accuracy.

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The integrated use of optical and InSAR data for urban land‐cover mapping

Cited by 39 publications

References 9 publications

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

Advanced Classification of Lands at TM and Envisat Images of Mongolia

Classifier Fusion of High-Resolution Optical and Synthetic Aperture Radar (Sar) Satellite Imagery for Classification in Urban Area

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