Journal ArticleDOI
Destriping multiple sensor imagery by improved histogram matching
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TLDR
In this article, the authors investigated the effect of sensor stripes in multisensor imagery and found that they can cause subsequent image classification to fail, if not removed properly, by removing the stripes.Abstract:
Sensor stripes evident in multisensor imagery can cause subsequent image classification to fail, if not removed properly. Of the destriping algorithms investigated, the one published by Horn and Wo...read more
Citations
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Book
Remote sensing, models, and methods for image processing
TL;DR: The Nature of Remote Sensing: Introduction, Sensor Characteristics and Spectral Stastistics, and Spatial Transforms: Introduction.
Journal ArticleDOI
Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images
TL;DR: A novel graph-regularized low-rank representation (LRR) destriping algorithm is proposed by incorporating the LRR technique and can both remove striping noise and achieve cleaner and higher contrast reconstructed results.
Journal ArticleDOI
A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images
Huanfeng Shen,Liangpei Zhang +1 more
TL;DR: The proposed algorithm has been tested using moderate resolution imaging spectrometer images for destriping and China-Brazil Earth Resource Satellite and QuickBird images for simulated inpainting and the results and quantitative analyses verify the efficacy of this algorithm.
Journal ArticleDOI
Destriping multisensor imagery with moment matching
TL;DR: An alternative algorithm is suggested which matches the gain and offset of each sensor to typical values, and which is resistant to the effects of outliers.
Journal ArticleDOI
Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping
TL;DR: This paper tentatively categorizes the stripes in remote sensing images in a more comprehensive manner and proposes to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral- spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension.
References
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Journal ArticleDOI
Destriping LANDSAT MSS images by histogram modification
TL;DR: Methods are presented for obtaining the required information directly from the statistics of the sensor outputs for destriping of LANDSAT Multispectral Scanner images, applying to images obtained with any multisensor line-scan camera.
Journal ArticleDOI
Automatic threshold selection from a histogram using the “exponential hull”
TL;DR: The exponential hull, a variation of the upper convex hull, is defined for a histogram and its properties allow it to be used in criteria for choosing the number and locations of thresholds for gray-level image segmentation from the image intensity histogram.
Journal ArticleDOI
U. Graf/H.-J. Henning/K. Stange, Formeln und Tabellen der mathematischen Statistik. XVI + 362 S. m. 78 Abb. Berlin/Heidelberg/New York 1966. Springer-Verlag. Preis geb. DM 58,50
Proceedings ArticleDOI
Destriping Of Landsat Data Using Power Spectral Filtering
TL;DR: In this paper, the basic spatial frequency composition of the degradation is determined from an easily computed one-dimensional power spectrum of the image and a convolutional restoration kernel is then created from a modified version of the power spectrum.
LANDSAT-4 multispectral scanner (MSS) subsystem radiometric characterization
TL;DR: In this paper, the multispectral band scanner (mass) and its spectral characteristics are described and methods are given for relating video digital levels on computer compatible tapes to radiance into the sensor.