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The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper.

ABSTRACT: Image fusion is a useful tool in integrating a high-resolution panchromaticimage (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolutionmultispectral image (HRMI). To date, many image fusion techniques have beendeveloped to try to improve the spatial resolution of the LRMI to that of the HRPI with itsspectral property reliably preserved. However, many studies have indicated that thereexists a trade- off between the spatial resolution improvement and the spectral propertypreservation of the LRMI, and it is difficult for the existing methods to do the best in bothaspects. Based on one minimization problem, this paper mathematically analyzes thetradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluatedthrough expanded spectral angle mapper (ESAM). Results clearly prove that all the testedmethods have this property.

SUBMITTER: Chen S

PROVIDER: S-EPMC3681148 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper.

Chen Shaohui S Su Hongbo H Zhang Renhua R Tian Jing J Yang Lihu L

Sensors (Basel, Switzerland) 20080124 1

Image fusion is a useful tool in integrating a high-resolution panchromaticimage (HRPI) with a low-resolution multispectral image (LRMI) to produce a highresolutionmultispectral image (HRMI). To date, many image fusion techniques have beendeveloped to try to improve the spatial resolution of the LRMI to that of the HRPI with itsspectral property reliably preserved. However, many studies have indicated that thereexists a trade- off between the spatial resolution improvement and the spectral prope ...[more]

PMID: 27879720

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Project description:Advances in remote sensing technology can help estimate biodiversity at large spatial extents. To assess whether we could use hyperspectral visible near-infrared (VNIR) spectra to estimate species diversity, we examined the correlations between species diversity and spectral diversity in early-successional abandoned agricultural fields in the Ridge and Valley ecoregion of north-central Virginia at the Blandy Experimental Farm. We established plant community plots and collected vegetation surveys and ground-level hyperspectral data from 350 to 1,025 nm wavelengths. We related spectral diversity (standard deviations across spectra) with species diversity (Shannon-Weiner index) and evaluated whether these correlations differed among spectral regions throughout the visible and near-infrared wavelength regions, and across different spectral transformation techniques. We found positive correlations in the visible regions using band depth data, positive correlations in the near-infrared region using first derivatives of spectra, and weak to no correlations in the red-edge region using either of the two spectral transformation techniques. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using spectral vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll was more varied within species than carotenoids and anthocyanins, contributing to the lack of correlation between species diversity and spectral diversity in the red-edge region. Interspecific differences in pigment levels, however, made it possible to differentiate these species remotely, contributing to the species-spectral diversity correlations. VNIR spectra can be used to estimate species diversity, but the relationships depend on the spectral region examined and the spectral transformation technique used.

Project description:BackgroundSparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts.PurposeThe purpose of this work is to address the previously mentioned challenges in current deep learning methods.MethodsA method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL-PICCS method in terms of reconstruction accuracy and generalizability.ResultsExtensive evaluation studies have demonstrated that: (1) quantitative reconstruction accuracy of DL-PICCS for individual patient is improved when it is compared with the deep learning methods and CS-based methods; (2) the false-positive lesion-like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL-PICCS reconstructed images; and (3) DL-PICCS enables a deep learning scheme to relax its working conditions to enhance its generalizability.ConclusionsDL-PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.

Dataset Information

The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper.

Publications

The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper.

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