Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method

Sathyabama, B.; Sankari, S. G. Siva; Nayagara, S.

doi:10.1109/ncvpripg.2013.6776256

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Orthogonal Matching Pursuit (Compressive sampling) algorithm is used to fuse low resolution multiple spectral images and high resolution panchromatic images. High resolution image is produced by convolving the spare coefficients (generated by correlating low resolution image patches with low resolution panchromatic) and high resolution panchromatic dictionary [14].…”

Section: Related Workmentioning

confidence: 99%

“…In [23], a procedure to fuse satellite images by methods for Compressive sampling matching Interest (CoSaMP) technique is proposed. This work produces inadequate coefficients by associating the low-resolution multispectral image with the low-goals skillet word reference.…”

Section: Related Workmentioning

confidence: 99%

“…Compressive Sensing (also known as compressed sensing, compressive sampling or sparse sampling) is a recent sensing approach that outperforms the Nyquist-Shannon sampling frequency in acquiring and reconstructing sparse and compressible signals [13,14,15]. From an application point of view, compressive sensing has demonstrated outstanding performance where: (1) we are restricted by the factor of energy consumption on sensing side (e.g., wireless sensor networks, deep space missions); (2) we are limited to use few sensors or spectral channels (i.e., nonvisible wavelengths, hyperspectral); (3) sensing is time-consuming (like medical imaging); or (4) measurement/sensing is too expensive (e.g., high-speed ADC), (5) where sensors are inhibited by data gaps (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Satellite data fusion of multiple observed XCO2 using compressive sensing

Nguyen

Shivadekar

Chukkapalli

et al. 2020

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

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When it entered into the era of big data, Earth observing systems developed into a new stage, namely characterized by low cost, multi-national, multi-sensor and multi-modal with varying spatial and spectral resolutions confronting new challenges and opportunities. Climate data records from multiple data sources are used to infer seasonal and interannual variations which will advance and promote the development of data fusion methods. Compressed sensing is a new framework in which data acquisition and data processing are merged. It provides a new fantastic way to handle multiple observations of the same field view from complementary remote sensing instruments, allowing us to recover information at very low signal-to-noise ratio.We will particularly point out that a Compressive Sensing based framework is flexible enough for combining the two measurement systems by fusing the data from the two satellites, NASA Orbiting Carbon Observatory -2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT) to calculate the interannual Net XCO2 variability over land for three latitudinal regions, Alaska/Canada, United States and the Amazon/Brazil. The OCO-2 design is optimized for sensitivity to XCO2 variations, with an unprecedented combination of spatial resolution (about 3km) with narrow nadir coverage, while GOSAT provides broader spatial coverage (10km) with wider scanning coverage. There are different temporal degradations of both instruments over time because GOSAT was launched in 2009 and OCO-2 was launched in 2014. Both instruments infer CO2 concentration from high-resolution measurements of reflected sunlight and use similar inversion algorithms to retrieve CO2 concentrations. Both are passive satellites providing on-orbit global measurements of the greenhouse gas, XCO2, for the years 2015 -2018. The results of the CS data fusion framework show that the fused data have Root Mean Square Error (RMSE) varying from 1.31 ppm to 4.12 ppm compared with original data, depending on the region of study and gridding resolution. Validation of fused data compared with AmeriFlux station towers observations shows RMSE of 2.68 ppm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Satellite data fusion of multiple observed XCO2 using compressive sensing

Nguyen

Shivadekar

Chukkapalli

et al. 2020

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

View full text Add to dashboard Cite

show abstract

“…Orthogonal Matching Pursuit (OMP) algorithm [25], [26] reconstructs the first K maximum in the frequency domain of ECG signal and acquires the reconstructed data through inverse Fourier transformation. Compressed Sampling Matching Pursuit (CoSaMP) algorithm [27], [28] applies similar principles of OMP. However, it chooses more atoms than OMP algorithm during iteration.…”

Section: ) Signal Reconstructionmentioning

confidence: 99%

Electrocardiogram Reconstruction Based on Compressed Sensing

et al. 2019

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Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much below the Nyquist rate. In this paper, we proposed novel CS algorithms for reconstructing under-sampled and compressed electrocardiogram (ECG) signal. In the proposed CS-ECG scheme, the ECG signal was first sub-sampled randomly and mapped onto a two-dimensional (2D) space by using Cut and Align (CAB), for the purpose of promoting sparsity. A nonlinear optimization model was then used to reconstruct the 2D signal. In the compression scheme, the ECG signal was mapped into the frequency domain, and the compression was achieved by a series of multiplying and accumulating between the original ECG and a Gaussian random matrix. For the reconstruction, two matching pursuits (MP) methods and two blocks sparse Bayesian learning (BSBL) methods were implemented and evaluated by the percentage rootmean-square difference (PRD). Based on the test with real ECG data, it was found that the proposed CS scheme was capable of faithfully reconstructing ECG signals with only 30% acquisition.INDEX TERMS Compressed sensing (CS), compression, electrocardiogram (ECG), reconstruction, subsampling.

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“…Satellite image fusion methods are mainly based on multiscale analysis [6], [7], [8], compressed sensing (CS) [9], [10], [11], intensity-hue-saturation (IHS) transform [12], [13], [14], and neural networks [15], [16], [17], [18], [19], [20], as described follows.…”

mentioning

confidence: 99%

TCCL-DenseFuse: Infrared and Water Vapor Satellite Image Fusion Model Using Deep Learning

Zhang

Guo

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article proposes an infrared and water vapor channel satellite image fusion model (TCCL-DenseFuse) based on DenseNet. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the distribution of temperature, and the water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. Studies have shown that infrared brightness temperature gradient and water vapor transport are closely related to tropical cyclone (TC) generation and evolution. In order to facilitate the fusion image obtained by the proposed fusion model to have a positive effect on TC monitoring and warning, the brightness temperature gradient and multiscale structural similarity in the satellite image are used to construct loss function of the proposed TCCL-DenseFuse model. The quality of the fused images is evaluated by seven objective quantitative indicators. In order to further verify the real application value of the proposed TCCL-DenseFuse model, fused images are also used to TC center location. Experimental results show that the proposed TCCL-DenseFuse fused satellite image not only contains rich information from both infrared and water vapor channels but also improves the accuracy of TC center positioning. The comprehensive performance of the proposed fusion model has certain advantages compared with similar fusion methods and can provide a reference for typhoon prevention and disaster mitigation. Index Terms-Deep learning, infrared image, satellite image fusion, tropical cyclone (TC) center positioning, water vapor image. I. INTRODUCTIONS ATELLITE image data commonly used in meteorological services are mainly derived from polar-orbiting and geostationary satellites. Because of their good temporal resolution and wide coverage, geostationary satellites are widely used in meteorological disaster monitoring and forecasting. Geostationary

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Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method

Cited by 8 publications

References 7 publications

Satellite data fusion of multiple observed XCO2 using compressive sensing

Satellite data fusion of multiple observed XCO2 using compressive sensing

Electrocardiogram Reconstruction Based on Compressed Sensing

TCCL-DenseFuse: Infrared and Water Vapor Satellite Image Fusion Model Using Deep Learning

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