1,978 research outputs found
Compressive Sensing for PAN-Sharpening
Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1]. In this paper, the proposed SparseFI algorithm is validated using UltraCam and WorldView-2 data. Visual and statistic analysis show superior performance of SparseFI compared to the existing conventional pan-sharpening methods in general, i.e. rich in spatial information and less spectral distortion. Moreover, popular quality assessment metrics are employed to explore the dependency on regularization parameters and evaluate the efficiency of various sparse reconstruction toolboxes
A Novel Metric Approach Evaluation For The Spatial Enhancement Of Pan-Sharpened Images
Various and different methods can be used to produce high-resolution
multispectral images from high-resolution panchromatic image (PAN) and
low-resolution multispectral images (MS), mostly on the pixel level. The
Quality of image fusion is an essential determinant of the value of processing
images fusion for many applications. Spatial and spectral qualities are the two
important indexes that used to evaluate the quality of any fused image.
However, the jury is still out of fused image's benefits if it compared with
its original images. In addition, there is a lack of measures for assessing the
objective quality of the spatial resolution for the fusion methods. So, an
objective quality of the spatial resolution assessment for fusion images is
required. Therefore, this paper describes a new approach proposed to estimate
the spatial resolution improve by High Past Division Index (HPDI) upon
calculating the spatial-frequency of the edge regions of the image and it deals
with a comparison of various analytical techniques for evaluating the Spatial
quality, and estimating the colour distortion added by image fusion including:
MG, SG, FCC, SD, En, SNR, CC and NRMSE. In addition, this paper devotes to
concentrate on the comparison of various image fusion techniques based on pixel
and feature fusion technique.Comment: arXiv admin note: substantial text overlap with arXiv:1110.497
Pansharpening techniques to detect mass monument damaging in Iraq
The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons
of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of
multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne
photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images
is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology,
for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular
application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in
unsafe area, is reported.
The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report
delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of
damages using VHR images
Viewing the Future? Virtual Reality In Journalism
Journalism underwent a flurry of virtual reality content creation, production and distribution starting in the final months of 2015. The New York Times distributed more than 1 million cardboard virtual reality viewers and released an app showing a spherical video short about displaced refugees. The Los Angeles Times landed people next to a crater on Mars. USA TODAY took visitors on a ride-along in the "Back to the Future" car on the Universal Studios lot and on a spin through Old Havana in a bright pink '57 Ford. ABC News went to North Korea for a spherical view of a military parade and to Syria to see artifacts threatened by war. The Emblematic Group, a company that creates virtual reality content, followed a woman navigating a gauntlet of anti- abortion demonstrators at a family planning clinic and allowed people to witness a murder-suicide stemming from domestic violence.In short, the period from October 2015 through February 2016 was one of significant experimentation with virtual reality (VR) storytelling. These efforts are part of an initial foray into determining whether VR is a feasible way to present news. The year 2016 is shaping up as a period of further testing and careful monitoring of potential growth in the use of virtual reality among consumers
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight network which trains very fast. Leveraging on this latter property,
we propose a target-adaptive usage modality which ensures a very good
performance also in the presence of a mismatch w.r.t. the training set, and
even across different sensors. The proposed method, published online as an
off-the-shelf software tool, allows users to perform fast and high-quality
CNN-based pansharpening of their own target images on general-purpose hardware
Water bodies' mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band
Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and Ă Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Most of the traditional convolutional neural networks (CNNs) implements
bottom-up approach (feed-forward) for image classifications. However, many
scientific studies demonstrate that visual perception in primates rely on both
bottom-up and top-down connections. Therefore, in this work, we propose a CNN
network with feedback structure for Solar power plant detection on
middle-resolution satellite images. To express the strength of the top-down
connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
used for solar power plant classification on multi-spectral satellite data.
Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network
(m-PCNN) for weakly-supervised localization of the solar power plants from the
features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
experimental results demonstrated promising results on both solar-power plant
image classification and detection task.Comment: 9 pages, 9 figures, 4 table
COMPARISON OF DIFFERENT FUSION ALGORITHMS IN URBAN AND AGRICULTURAL AREAS USING SAR (PALSAR AND RADARSAT) AND OPTICAL (SPOT) IMAGES
Image fusion techniques of remote sensing data are formal frameworks for merging and using images originating from different sources. This research investigates the quality assessment of Synthetic Aperture Radar (SAR) data fusion with optical imagery. Two different SAR data from different sensors namely RADARSAT-1 and PALSAR were fused with SPOT-2 data. Both SAR data have the same resolutionsand polarisations; however images were gathered in different frequencies as C band and L band respectively. This paper contributes to the comparative evaluation of fused data for understanding the performance of implemented image fusionalgorithms such as Ehlers, IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), two dimensional DWT (Discrete Wavelet Transformation), and PCA (Principal Component Analysis) techniques. Quality assessments of fused imageswere performed both qualitatively and quantitatively. For the statistical analysis; bias, correlation coefficient (CC), difference in variance (DIV), standard deviation difference (SDD), universal image quality index (UIQI) methods were applied on the fused images. The evaluations were performed by categorizing the test area into two as “urban” and “agricultural”. It has been observed that some of the methodshave enhanced either the spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. In general we noted that Ehlers’ spectral quality is far better than those of the other methods. HPF performs almost best in agricultural areas for both SAR images
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