scholarly journals Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7100
Author(s):  
Katarzyna Siok ◽  
Ireneusz Ewiak ◽  
Agnieszka Jenerowicz
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Traditional Approach ◽  
Spectral Band ◽  
Spectral Characteristics ◽  
Quality Analysis ◽  
Time Shift ◽  
Multispectral Images ◽  
Spectral Quality ◽  
Imaging Data

The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.

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.

Intensity-modulated Parametric Mapping for Simultaneous Display of Rapid Dynamic and High-Spatial-Resolution Breast MR Imaging Data

Radiographics ◽  
2001 ◽  
Vol 21 (1) ◽  
pp. 217-226 ◽  
Author(s):  
A. Tony Agoston ◽  
Bruce L. Daniel ◽  
Robert J. Herfkens ◽  
Debra M. Ikeda ◽  
Robyn L. Birdwell ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Imaging Data ◽  
Parametric Mapping ◽  
Intensity Modulated ◽  
Breast Mr

scholarly journals PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2804
Author(s):  
Junmin Liu ◽  
Yunqiao Feng ◽  
Changsheng Zhou ◽  
Chunxia Zhang
Keyword(s):  
Neural Network ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
State Of The Art ◽  
Spectral Information ◽  
Data Sets ◽  
Multispectral Images ◽  
Image Prior ◽  
Adaptive Weight ◽  
Component Substitution

Pansharpening is a typical image fusion problem, which aims to produce a high resolution multispectral (HRMS) image by integrating a high spatial resolution panchromatic (PAN) image with a low spatial resolution multispectral (MS) image. Prior arts have used either component substitution (CS)-based methods or multiresolution analysis (MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose a pansharpening weight network (PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainable neural network (NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

scholarly journals Coronal Transients in Radio and X-rays

1996 ◽  
Vol 154 ◽  
pp. 15-22
Author(s):  
Mukul R. Kundu
Keyword(s):  
Active Region ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
X Rays ◽  
Imaging Data ◽  
X Ray ◽  
Type Iii ◽  
Wave Data ◽  
Spatially Resolved ◽  
Radio Imaging

AbstractWe present a summary of several studies of transient coronal phenomena based upon high spatial resolution radio imaging data along with Yohkoh SXT and HXT observations. In addition to normal flares the studies also involve such exotic events as active region transient brightenings (ARTB) and coronal jets and bright points. We provide evidence of nonthermal processes in flaring X-ray bright points from spatially resolved meter-wave data, existence and propagation of type III burst emitting electrons in coronal jets, radio signatures of ARTB’s, and beaming of electrons producing microwave and hard X-rays. The implications of these observations are discussed.

scholarly journals VISIR-SAT – A PROSPECTIVE MICRO-SATELLITE BASED MULTI-SPECTRAL THERMAL MISSION FOR LAND APPLICATIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1283-1289 ◽  
Author(s):  
G. Ruecker ◽  
G. Menz ◽  
S. Heinemann ◽  
M. Hartmann ◽  
D. Oertel
Keyword(s):  
High Temperature ◽  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
High Spatial Resolution ◽  
Spectral Imaging ◽  
Thermal Infrared ◽  
Fire Disturbance ◽  
Image Processing Technique ◽  
Imaging Data

Current space-borne thermal infrared satellite systems aimed at land surface remote sensing retain some significant deficiencies, in particular in terms of spatial resolution, spectral coverage, number of imaging bands and temperature-emissivity separation. The proposed VISible-to-thermal IR micro-SATellite (VISIR-SAT) mission addresses many of these limitations, providing multi-spectral imaging data with medium-to-high spatial resolution (80m GSD from 800 km altitude) in the thermal infrared (up to 6 TIR bands, between 8 and 11μm) and in the mid infrared (1 or 2 MIR bands, at 4μm). These MIR/TIR bands will be co-registered with simultaneously acquired high spatial resolution (less than 30 m GSP) visible and near infrared multi-spectral imaging data. To enhance the spatial resolution of the MIR/TIR multi-spectral imagery during daytime, data fusion methods will be applied, such as the Multi-sensor Multi-resolution Technique (MMT), already successfully tested over agricultural terrain. This image processing technique will make generation of Land Surface Temperature (LST) EO products with a spatial resolution of 30 x 30 m<sup>2</sup> possible. For high temperature phenomena such as vegetation- and peat-fires, the Fire Disturbance Essential Climate Variables (ECV) “Active fire location” and “Fire Radiative Power” will be retrieved with less than 100 m spatial resolution. Together with the effective fire temperature and the spatial extent even for small fire events the innovative system characteristics of VISIR-SAT go beyond existing and planned IR missions. <br><br> The comprehensive and physically high-accuracy products from VISIR-SAT (e.g. for fire monitoring) may synergistically complement the high temperature observations of Sentinel-3 SLSTR in a unique way. Additionally, VISIR-SAT offers a very agile sensor system, which will be able to conduct intelligent and flexible pointing of the sensor’s line-of-sight with the aim to provide global coverage of cloud free imagery every 5&ndash;10 days with only one satellite (using near real time cloud cover information). VISIRSAT may be flown in convoy with Sentinel-3 and/or Sentinel-2.

Sign in / Sign up

Export Citation Format

Share Document