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 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 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 Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5317 ◽  
Author(s):  
Moonyoung Kwon ◽  
Sangjun Han ◽  
Kiwoong Kim ◽  
Sung Chan Jun
Keyword(s):  
Spatial Resolution ◽  
Evoked Potential ◽  
Mean Squared Error ◽  
Simulated Data ◽  
Super Resolution ◽  
Auditory Evoked Potential ◽  
Brain Dynamics ◽  
Deep Convolutional Neural Networks ◽  
Squared Error ◽  
Eeg Data

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach’s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.

scholarly journals Accuracy of the TanDEM-X Digital Elevation Model for Coastal Geomorphological Studies in Patagonia (South Argentina)

2019 ◽  
Vol 11 (15) ◽  
pp. 1767 ◽  
Author(s):  
Francesca Pasquetti ◽  
Monica Bini ◽  
Andrea Ciampalini
Keyword(s):  
Digital Elevation Model ◽  
Spatial Resolution ◽  
Mean Squared Error ◽  
Low Cost ◽  
High Agreement ◽  
Squared Error ◽  
Digital Elevation ◽  
Vertical Error ◽  
Global Coverage ◽  
Elevation Model

The aim of this paper is to evaluate the usefulness of TanDEM-X DEM (digital elevation model) for remote geomorphological analysis in Argentinian Patagonia. The use of a DEM with appropriate resolution and coverage might be very helpful and advantageous in vast and hardly accessible areas. TanDEM-X DEM could represent an unprecedented opportunity to identify geomorphological features because of its global coverage, ~12 m spatial resolution and low cost. In this regard, we assessed the vertical accuracy of TanDEM-X DEM through comparison with Differential Global Positioning System (DGPS) datasets collected in two areas of the Patagonia Region during a field survey; we then investigated different types of landforms by creating the elevation profiles. The comparison indicates a high agreement between TanDEM-X DEM and reference values, with a mean absolute vertical error (MAE) of 0.53 m, and a root mean squared error (RMSE) of 0.73 m. The results of landform analysis show an appropriate spatial resolution to detect different features such as beach ridges, which are impossible to delineate with other lower resolution DEMs. For these reasons, TanDEM-X DEM constitutes a useful tool for detailed geomorphological analyses in Argentinian Patagonia.

scholarly journals Optical SAR Images Fusion: Comparative Analysis of Resulting Images Data

2018 ◽  
Vol 215 ◽  
pp. 01002
Author(s):  
Yuhendra ◽  
Minarni
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Mean Squared Error ◽  
Statistical Parameter ◽  
Speckle Noise ◽  
Sar Images ◽  
Squared Error ◽  
Fused Image ◽  
Fusion Methods ◽  
High Pass

Image fusion is a useful tool for integrating low spatial resolution multispectral (MS) images with a high spatial resolution panchromatic (PAN) image, thus producing a high resolution multispectral image for better understanding of the observed earth surface. A main proposed the research were the effectiveness of different image fusion methods while filtering methods added to speckle suppression in synthetic aperture radar (SAR) images. The quality assessment of the filtering fused image implemented by statistical parameter namely mean, standard deviation, bias, universal index quality image (UIQI) and root mean squared error (RMSE). In order to test the robustness of the image quality, either speckle noise (Gamma map filter) is intentionally added to the fused image. When comparing and testing result, Gram Scmidth (GS) methods have shown better results for good colour reproduction, as compared with high pass filtering (HPF). And the other hands, GS, and wavelet intensity hue saturation (W-IHS) have shown the preserving good colour with original image for Landsat TM data.

scholarly journals SCENE CLASSFICATION BASED ON THE SEMANTIC-FEATURE FUSION FULLY SPARSE TOPIC MODEL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 451-457
Author(s):  
Qiqi Zhu ◽  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Semantic Feature ◽  
Scene Classification ◽  
Probabilistic Topic Model ◽  
Sparse Topic Model

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features – the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

scholarly journals An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

2019 ◽  
Vol 9 (23) ◽  
pp. 5234 ◽  
Author(s):  
Rahimzadeganasl ◽  
Alganci ◽  
Goksel
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Critical Role ◽  
Study Region ◽  
High Resolution Satellite Images ◽  
Very High ◽  
Component Substitution ◽  
Cielab Color

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

Analysis of Layered Structures at High Spatial Resolution Using Energy Filtered Imaging

1998 ◽  
Vol 523 ◽  
Author(s):  
P.L. Flaitz ◽  
J. Bruley
Keyword(s):  
Spatial Resolution ◽  
Spatial Information ◽  
High Spatial Resolution ◽  
Spatial Dimension ◽  
Imaging Systems ◽  
Multivariate Statistical ◽  
Edge Structure ◽  
Small Probe ◽  
High Spatial Dimension ◽  
Energy Information

AbstractThe development of energy filtered imaging systems for the TEM has opened new approaches to analyzing structures with very small dimensions. One of the benefits of such systems is the ability to form an EELS spectrum image containing energy information on one axis and spatial information on the other axis. By dissecting such an image in the spatial dimension, it is possible to generate a line profile across a layered structure at high spatial dimension without the need for a small probe. We have applied this approach to two structures typical of semiconductors, the Si/SiO2/Si interfaces in a gate oxide and the Ti/SiO2 interface for Al barrier metallization, which illustrate the spatial resolution possible with this technique. To analyze the spectrum images, the element specific near edge structure (ELNES) data are processed by conventional EELS background routines and multivariate statistical techniques using MATLAB software to extract both profiles of principal bonding components and composition.

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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