scholarly journals Interferometric processing of TanDEM-X bistatic SAR data using GAMMA – implementation outline

2021 ◽  
Vol 33 ◽  
pp. 101-123
Author(s):  
Konstantin Metodiev
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Phase Unwrapping ◽  
Direct Control ◽  
High Scale ◽  
Mountainous Regions ◽  
Bistatic Sar ◽  
Implementation Approach ◽  
Sar Data ◽  
Test Feasibility

This article represents a showcase of two different coding approaches with GAMMA, used to calculate topographic and differential phases from high resolution TanDEM-X bistatic data, provided by DLR. The first implementation approach comprises “BASH” scripting in Linux environment, having direct control of the GAMMA executables. The second approach is utilization of the PyroSAR framework, via GAMMA-API, in Python environment. Two spatial resolution scales are used – of 4 and 12 meters, to test feasibility of TanDEM-X InSAR output products in mountainous forest in rugged region. The first approach allowed thorough processing with abundant GAMMA output, whereas the high scale PyroSAR framework via GAMMA-API showed fast implementation. Comparison over 4 and 12m spatial resolution products showed good feasibility with strong influence from topography. Intense multi-looking resolved better connection of coherence amplitude to the volume decorrelation in canopy, despite preserving high resolution reveals plenty of details in land cover. Differential height calculation, without phase unwrapping, showed its significance in data processing over mountainous regions. Intensities normalization and terrain flattening showed good performance in both resolution scales. Finally, utilization of GAMMA in InSAR processing of high resolution TanDEM-X bistatic SAR data showed good feasibility and flexibility to derive interferometric products.

scholarly journals Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data

2021 ◽  
Vol 13 (4) ◽  
pp. 732
Author(s):  
Ryota Nomura ◽  
Kazuo Oki
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Target Area ◽  
Modis Ndvi ◽  
Acceptable Accuracy ◽  
Sar Data ◽  
Sentinel 2

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

scholarly journals A Long-Term, High-Resolution Wetland Dataset over the Amazon Basin, Downscaled from a Multiwavelength Retrieval Using SAR Data

2013 ◽  
Vol 14 (2) ◽  
pp. 594-607 ◽  
Author(s):  
Filipe Aires ◽  
Fabrice Papa ◽  
Catherine Prigent
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Land Surface ◽  
Amazon Basin ◽  
Near Infrared ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Sar Data ◽  
Ocean Topography

Abstract A climatology of wetlands has been derived at a low spatial resolution (0.25° × 0.25° equal-area grid) over a 15-yr period by combining visible and near-infrared satellite observations and passive and active microwaves. The objective of this study is to develop a downscaling technique able to retrieve wetland estimations at a higher spatial resolution (about 500 m). The proposed method uses an image-processing technique applied to synthetic aperture radar (SAR) information about the low and high wetland season. This method is tested over the densely vegetated basin of the Amazon. The downscaling results are satisfactory since they respect the spatial hydrological features of the SAR data and the temporal evolution of the low-resolution wetland estimates. A new long-term and high-resolution wetland dataset has been generated for 1993–2007 for the Amazon basin. This dataset represents a new and unprecedented source of information for climate and land surface modeling of the Amazon and for the definition of future hydrology-oriented satellite missions such as Surface Water and Ocean Topography (SWOT).

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

High Resolution Current Imaging by Direct Magnetic Field Sensing

2003 ◽  
Author(s):  
S.I. Woods ◽  
Nesco M. Lettsome ◽  
A.B. Cawthorne ◽  
L.A. Knauss ◽  
R.H. Koch
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Promise ◽  
Magnetoresistive Sensor ◽  
Magnetic Sensitivity ◽  
Current Lines ◽  
Scanning Squid ◽  
Scanning Fiber ◽  
Ferromagnetic Probe

Abstract Two types of magnetic microscopes have been investigated for use in high resolution current mapping. The scanning fiber/SQUID microscope uses a SQUID sensor coupled to a nanoscale ferromagnetic probe, and the GMR microscope employs a nanoscale giant magnetoresistive sensor. Initial scans demonstrate that these microscopes can resolve current lines less than 10 µm apart with edge resolution of 1 µm. These types of microscopes are compared with the performance of a standard scanning SQUID microscope and with each other with respect to spatial resolution and magnetic sensitivity. Both microscopes show great promise for identifying current defects in die level devices.

Bistatic SAR Data Focusing Using a Frequency Scaling Algorithm Based on an Analytical Spectrum in Tandem Configuration

2011 ◽  
Vol 33 (6) ◽  
pp. 1447-1452
Author(s):  
Shi-chao Chen ◽  
Qi-song Wu ◽  
Ming Liu ◽  
Meng-dao Xing ◽  
Zheng Bao
Keyword(s):  
Frequency Scaling ◽  
Tandem Configuration ◽  
Bistatic Sar ◽  
Sar Data

scholarly journals Elevational Movement of Vegetation Greenness on the Tibetan Plateau: Evidence from the Landsat Satellite Observations during the Last Three Decades

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 161
Author(s):  
Liheng Lu ◽  
Xiaoqian Shen ◽  
Ruyin Cao
Keyword(s):  
Tibetan Plateau ◽  
Spatial Resolution ◽  
Climate Warming ◽  
Vertical Movement ◽  
The Tibetan Plateau ◽  
Upward Movement ◽  
Vegetation Growth ◽  
Vegetation Greenness ◽  
Mountainous Regions ◽  
Vegetation Activities

The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

River detection in high-resolution SAR data using the Frangi filter and shearlet features

2019 ◽  
Vol 10 (10) ◽  
pp. 949-958 ◽  
Author(s):  
Yuhan Liu ◽  
Lingbing Peng ◽  
Suqi Huang ◽  
Xiaoyang Wang ◽  
Yuqing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Sar Data ◽  
Frangi Filter

scholarly journals ZnWO4 Nanoparticle Scintillators for High Resolution X-ray Imaging

Nanomaterials ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1721
Author(s):  
Heon Yong Jeong ◽  
Hyung San Lim ◽  
Ju Hyuk Lee ◽  
Jun Heo ◽  
Hyun Nam Kim ◽  
...  
Keyword(s):  
Particle Size ◽  
Zinc Oxide ◽  
High Resolution ◽  
Tungsten Oxide ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
X Ray ◽  
X Ray Imaging ◽  
Average Size ◽  
And Tungsten

The effect of scintillator particle size on high-resolution X-ray imaging was studied using zinc tungstate (ZnWO4) particles. The ZnWO4 particles were fabricated through a solid-state reaction between zinc oxide and tungsten oxide at various temperatures, producing particles with average sizes of 176.4 nm, 626.7 nm, and 2.127 μm; the zinc oxide and tungsten oxide were created using anodization. The spatial resolutions of high-resolution X-ray images, obtained from utilizing the fabricated particles, were determined: particles with the average size of 176.4 nm produced the highest spatial resolution. The results demonstrate that high spatial resolution can be obtained from ZnWO4 nanoparticle scintillators that minimize optical diffusion by having a particle size that is smaller than the emission wavelength.

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