Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

Berger, Christian; Voltersen, M.; Eckardt, R. C.; Eberle, Jonas; Heyer, T.; Salepci, N.; Hese, S.; Schmullius, C.; Tao, Junyi; Auer, Stefan; Bamler, Richard; Ewald, K.; Gartley, Michael; Jacobson, J.; Buswell, Alan T.; Du, Qian; Pacifici, Fabio

doi:10.1109/jstars.2013.2245860

Cited by 82 publications

(35 citation statements)

References 91 publications

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“…Nonetheless, the resolution of the imagery and the heterogeneity characteristic of urban landscapes make it difficult to automatically map detailed urban lands solely using optical remote sensing methods (Cockx, Voorde, & Canters, 2014). The use of ancillary datasets such as census data, road networks, impervious surface coverages, landscape metrics, land parcel attributes, and radar data were recently documented to improve urban classifications (Abed & Kaysi, 2003;Berger et al, 2013;Chaudhry & Mackaness, 2008;Hermosilla, Palomar-V azquez, Balaguer-Beser, Balsa-Barreiro, & Ruiz, 2014;Schneider, Friedl, & Potere, 2014;Soergel, 2010;Wu, Qiu, Usery, & Wang, 2009). Fractal methods have also been documented as a successful component in aiding in locating UBs (Tannier & Thomas, 2013;Tannier, Thomas, Vuidel, & Frankhauser, 2011).…”

Section: Introductionmentioning

confidence: 99%

Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

Tong

Frazier

et al. 2015

Habitat International

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a b s t r a c tPeriodic monitoring and multi-scale characterization of urban sprawl is essential for improving urban planning and development. However, historical sprawl analysis is not well suited for the neourbanization occurring in most cities in China due to the limited data available. This paper proposes a concise and cost-effective method for automating the extraction of urban boundaries (UBs). The method uses integrated land-use information entropy (LUIE) model along with ordinary Kriging based on a gridded land-use map derived from Landsat imagery to extract UBs. Results indicate that overall extraction accuracies greater than 90% were obtained using an 800 m-resolution LUIE combined with Kriging. The method was applied to identify UBs in Wuhan, China during 1987e2010, and the UBs were characterized at multiple scales and analyzed using landscape metrics. Results show varied landscape dynamics at local administrative and city scales. The study demonstrates that the method for UB identification and multi-scale analysis has the potential to contribute to sprawl monitoring and measurement at multiple spatial scales. Moreover, the findings from this study can potentially guide policy makers and urban planners tasked with understanding and controlling development occurring under neo-urbanization strategies in China.

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Section: Introductionmentioning

confidence: 99%

Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

Tong

Frazier

et al. 2015

Habitat International

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“…Evaluating the spatial and spectral details of images after applying an image fusion technique is an important step [51]. Guided filtration process for image fusion is presented in [52].Multimodal and multitemporal image fusion method can be analyzed in [53]. ICA independent component analysis approach can also be utilized for performing a fusion of sequence of images [54].…”

Section: Image Fusion Systemsmentioning

confidence: 99%

Image Fusion Methods: A Survey

Masood¹,

Sharif²,

Yasmin³

et al. 2017

JESTR

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Image fusion is an approach which is used to amalgamate the corresponding features in a sequence of input images to a single composite image that preserves all the significant features of the input images. Image fusion is also known as pansharpening. It is a method which is used to integrate and add the geometric detail of a high-resolution panchromatic (Pan) image and the information of color of a low-resolution multispectral (MS) image for the production of a high-resolution MS image. This methodology is mainly most important and significant for any large-scale applications. Image fusion classification based on its systems (models and algorithms) are considered and overviewed in this survey. The basic two algorithms categories are analyzed and compared to each other, in the analysis and discussion section the major points to be considered while performing image fusion are highlighted.

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“…In Bayesian-based spectral change detection method, the key dates and periods were missed from the record due to the climatic conditions. Berger et al [23] took the SAR and air bone Light Detection and Ranging (LiDAR) data to improve the surface reluctance retrievals of the optical data. They also finally demonstrated the usefulness of LiDAR fused with optical data.…”

Section: Related Workmentioning

confidence: 99%

“…The image fusion performance (I) between input images (A, B) to F with pixels (p) is given by ( , ) = ∑ ( , ) ( , ) ( ) ( ) , (23) ( , ) = ∑ ( , ) ( , ) ( ) ( ) , (24) The MI between the input images ( ) and F is given by…”

Section: Mutual Informationmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization with Affine Transformation Approach for Cloud Free Multi-Temporal Image Registration

Prabu

V.S

2016

ELCVIA

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Image Registration (IR) is the process of transformation of different data into the coordinate system and provides the geometric alignment of two images used in the computer vision, medical imaging and remote sensing applications. An image registration is an important stage in multi-temporal image processing since, the recovery of information from cloud shadow is difficult. Traditionally, the Demons, Combined Registration and Segmentation (CRS) approach, Markov Random Field (MRF) and Mutual Information (MI) based approaches offers more computational complexity, minimum edge preservation measure (QAB/F) during image registration process. To maximize the quality of edge preservation measure and MI with minimum computational time, this paper proposes hybrid Particle Swarm Optimization (PSO)-Affine Transformation (AT) technique for an image registration. An enhanced registration process and the cloud removal technique are proposed for quality improvement of an image. Initially, Gaussian filtering in the preprocessing stage removes the noises present in an image. The proposed PSO extracts the matching points between the reference image and target image in the multitemporal image dataset. Then, the AT on extracted matching points provides the specific feature points from main features. Finally, the Relevance Vector Machine (RVM) classification forms the cluster of specific feature points. The extracted feature points from PSO-AT maximize the quality of edge preservation and MI with efficient cloud removal. The comparative analysis with the traditional methods of Control Point -Least Square (CP-LS), MultiFocus Image Fusion (MFIF) and Discrete Wavelet Transform (DWT) on the parameters of QAB/F and MI shows the effectiveness of proposed PSO-AT.

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Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

Cited by 82 publications

References 91 publications

Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

Urban boundary extraction and sprawl analysis using Landsat images: A case study in Wuhan, China

Image Fusion Methods: A Survey

A Hybrid Particle Swarm Optimization with Affine Transformation Approach for Cloud Free Multi-Temporal Image Registration

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