scholarly journals Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5308
Author(s):  
Fernando Pérez-Bueno ◽  
Miguel Vega ◽  
Javier Mateos ◽  
Rafael Molina ◽  
Aggelos K. Katsaggelos
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Synthetic Data ◽  
Multispectral Image ◽  
Experimental Results ◽  
Spectral Information ◽  
Variational Bayesian ◽  
Observation Process ◽  
Image Priors

Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.

scholarly journals Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

2019 ◽  
Vol 11 (9) ◽  
pp. 1005
Author(s):  
Jiahui Qu ◽  
Yunsong Li ◽  
Qian Du ◽  
Wenqian Dong ◽  
Bobo Xi
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Mean Squared Error ◽  
High Spatial Resolution ◽  
Experimental Results ◽  
Spectral Information ◽  
Morphological Operation ◽  
Effective Technique ◽  
Squared Error ◽  
Homomorphic Filtering

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.

scholarly journals Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane

2017 ◽  
Vol 35 (1) ◽  
pp. 82-91
Author(s):  
Cesar Edwin García ◽  
David Montero ◽  
Hector Alberto Chica
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Early Stage ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Crop Productivity ◽  
Spectral Information ◽  
Nitrogen Effect ◽  
Sugarcane Yield

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

scholarly journals A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8

2019 ◽  
Vol 11 (22) ◽  
pp. 2606 ◽  
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng
Keyword(s):  
Spatial Resolution ◽  
Mean Squared Error ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spectral Angle Mapper ◽  
Learning Capacity ◽  
Panchromatic Image ◽  
Highly Nonlinear ◽  
Construction Tasks

The increasing availability of sensors enables the combination of a high-spatial-resolution panchromatic image and a low-spatial-resolution multispectral image, which has become a hotspot in recent years for many applications. To address the spectral and spatial distortions that adversely affect the conventional methods, a pan-sharpening method based on a convolutional neural network (CNN) architecture is proposed in this paper, where the low-spatial-resolution multispectral image is upgraded and integrated with the high-spatial-resolution panchromatic image to produce a new multispectral image with high spatial resolution. Based on the pyramid structure of the CNN architecture, the proposed method has high learning capacity to generate more representative and robust hierarchical features for construction tasks. Moreover, the highly nonlinear fusion process can be effectively simulated by stacking several linear filtering layers, which is suitable for learning the complex mapping relationship between a high-spatial-resolution panchromatic and low-spatial-resolution multispectral image. Both qualitative and quantitative experimental analyses were carried out on images captured from a Landsat 8 on-board operational land imager (LOI) sensor to demonstrate the method’s performance. The results regarding the sensitivity analysis of the involved parameters indicate the effects of parameters on the performance of our CNN-based pan-sharpening approach. Additionally, our CNN-based pan-sharpening approach outperforms other existing conventional pan-sharpening methods with a more promising fusion result for different landcovers, with differences in Erreur Relative Globale Adimensionnelle de Synthse (ERGAS), root-mean-squared error (RMSE), and spectral angle mapper (SAM) of 0.69, 0.0021, and 0.81 on average, respectively.

scholarly journals Enhanced Back-Projection as Postprocessing for Pansharpening

2019 ◽  
Vol 11 (6) ◽  
pp. 712 ◽  
Author(s):  
Junmin Liu ◽  
Jing Ma ◽  
Rongrong Fei ◽  
Huirong Li ◽  
Jiangshe Zhang
Keyword(s):  
Spatial Resolution ◽  
Modulation Transfer Function ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
Back Projection ◽  
Modulation Transfer ◽  
Panchromatic Image ◽  
Consistency Property ◽  
High Pass ◽  
Satellite Sensor

Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods.

scholarly journals Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

2020 ◽  
Vol 12 (6) ◽  
pp. 993 ◽  
Author(s):  
Chen Yi ◽  
Yong-qiang Zhao ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
Resolution Enhancement ◽  
Multispectral Image ◽  
Observation Model ◽  
Multispectral Images ◽  
Spectral Matrix ◽  
Spectral Bands ◽  
Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

scholarly journals Lunar Image Fusion using Wavelet Transform

2013 ◽  
pp. 211-215
Author(s):  
Dr.Vani. K ◽  
Anto. A. Micheal
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Lunar Surface ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Mineral Mapping

This paper is an attempt to combine high resolution panchromatic lunar image with low resolution multispectral lunar image to produce a composite image using wavelet approach. There are many sensors that provide us image data about the lunar surface. The spatial resolution and spectral resolution is unique for each sensor, thereby resulting in limitation in extraction of information about the lunar surface. The high resolution panchromatic lunar image has high spatial resolution but low spectral resolution; the low resolution multispectral image has low spatial resolution but high spectral resolution. Extracting features such as craters, crater morphology, rilles and regolith surfaces with a low spatial resolution in multispectral image may not yield satisfactory results. A sensor which has high spatial resolution can provide better information when fused with the high spectral resolution. These fused image results pertain to enhanced crater mapping and mineral mapping in lunar surface. Since fusion using wavelet preserve spectral content needed for mineral mapping, image fusion has been done using wavelet approach.

Aeroelastic Investigation of an Annular Transonic Compressor Cascade: Experimental Results

2015 ◽  
Author(s):  
Virginie Anne Chenaux ◽  
Achim Zanker ◽  
Peter Ott
Keyword(s):  
Spatial Resolution ◽  
Transonic Flow ◽  
High Spatial Resolution ◽  
Experimental Results ◽  
Quality Data ◽  
Test Facility ◽  
Flow Conditions ◽  
Compressor Cascade ◽  
High Quality ◽  
Transonic Compressor

A reliable determination of the unsteady aerodynamic loads acting on the blades is essential to predict the aeroelastic stability of vibrating compressor cascades with accuracy. At transonic flow conditions, the vibration of the shock may change the blade aeroelastic behavior. Numerical tools still have difficulties to capture the physics associated to this effect. In order to increase the prediction’s accuracy, high quality experimental data at high spatial resolution is therefore required to enable the calibration and validation of these tools. Within the frame of the European project FUTURE, experimental aeroelastic investigations were performed on a transonic compressor cascade in the Non-Rotating Annular Test Facility at EPFL. Associated to the measurements, the numerical flutter prediction procedure was applied. This paper focuses on the experimental results. The experimental database gained during the project is presented and aims at helping the aeroelastic community to develop and improve their flutter prediction capabilities. The test model consists of twenty prismatic blades. Each blade of the cascade assembly was mounted on an elastic spring element enabling harmonic bending vibrations in the twenty possible cascade’s travelling wave modes. Large efforts were made to improve the measuring techniques and to provide high quality data at relatively high spatial resolution. For various sub- and transonic flow conditions, steady-state and unsteady blade surface pressure distributions were measured to evaluate the local contributions to the blade stability in terms of local aerodynamic work. The blade global aerodynamic stability is determined applying an integration of all unsteady pressure signals measured over the airfoil.

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