Panchromatic Data Research Articles

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data

Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.

Phobos photometric properties from Mars Express HRSC observations

Aims. This study aims to analyze Phobos’ photometric properties using Mars Express mission observations to support the Martian Moons exploration mission (MMX) devoted to the investigation of the Martian system and to the return of Phobos samples. Methods. We analyzed resolved images of Phobos acquired between 2004 and 2022 by the High Resolution Stereo Camera (HRSC) on board the Mars Express spacecraft at a resolution ranging from ~30 m px−1 to 330 m px−1. We used data acquired with the blue, green, red, and IR filters of HRSC and the panchromatic data of the Super Resolution Channel (SRC). The SRC data are unique because they cover small phase angles (0.2–10°), permitting the investigation of the Phobos opposition effect. We simulated illumination and geometric conditions for the different observations using the Marx Express and the camera spice kernels provided by the HRSC team. We performed photometric analysis using the Hapke model for both integrated and disk-resolved data. Results. The Phobos phase function is characterized by a strong opposition effect due to shadow hiding, with an amplitude and a half-width of the opposition surge of 2.28±0.03 and 0.0573±0.0001, respectively. Overall, the surface of Phobos is dark, with a geometric albedo of 6.8% in the green filter and backscattering. Its single-scattering albedo (SSA) value (7.2% in the green filter) is much higher than what has been found for primitive asteroids and cometary nuclei and is close to the values reported in the literature for Ceres. We also found a surface porosity of 87%, indicating the presence of a thick dust mantle or of fractal aggregates on the top surface. The SSA maps revealed high reflectance variability, with the blue unit area in the northeast Stickney rim being up to 65% brighter than average, while the Stickney floor is among the darkest regions, with reflectance 10 to 20% lower than average. Photometric modeling of the regions of interest selected in the red and blue units indicates that red unit terrains have a stronger opposition effect and a smaller SSA value than the blue ones, but they have similar porosity and backscattering properties. Conclusions. The HRSC data provide a unique investigation of the Phobos phase function and opposition surge, which is valuable information for the MMX observational planning. The Phobos opposition surge, surface porosity, phase integral, and spectral slope are very similar to the values observed for the comet 67P and for Jupiter family comets in general. Based on these similarities, we formulate a hypothesis that the Mars satellites might be the results of a binary or bilobated comet captured by Mars.

Spatial enhancement of Landsat-9 land surface temperature imagery by Fourier transformation-based panchromatic fusion

ABSTRACT Landsat-9 Panchromatic (PAN) band images are 7 times finer than land surface temperature (LST) photos of the Thermal Infrared (TIR) band. PAN bands have superior image resolution, consistency, and less ambiguity than TIR bands due to their smaller pixel sizes. Image fusion enhances images by combining data from several sources to make them better. Image fusion methods cannot combine PAN and TIR bands. This research proposes Fourier Transformation-based fusion (FTBF) to merge PAN and TIR band data to spatially enhance Landsat-9 LST images from 100 m to 15 m resolution. Fourier transformation integrates frequency domain filtering and spatial matching in FTBF. In-situ infrared thermometers data loggers verified temperature and picture quality parameters for FTBF algorithm fused image thermal points. Comparing downscaled LST with ground truth points yielded an RMSE of 0.18 and a correlation of 0.93. Eight qualitative and quantitative characteristics reveal that FTBF fusion methods improve TIR picture spatial resolution and preserve original LST data thermal attributes. LST-Pan fusion can detect surface temperature change for land-use change, fire detection, forest fire, agricultural analysis, crop management, and flood mapping at finer scales.

Measuring the Dust Attenuation Curves of SINGS/KINGFISH Galaxies Using Swift/UVOT Photometry

We present Swift/Ultraviolet Optical Telescope (UVOT) integrated light photometry of the Spitzer Infrared Nearby Galaxies Survey (SINGS) and the Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel (KINGFISH) samples of nearby galaxies. Combining the Swift/UVOT data with archival photometry, we investigate a variety of dust attenuation curves derived using MCSED, a flexible spectral energy distribution (SED) fitting code. We fit the panchromatic data using three different star formation history (SFH) parameterizations: a decaying exponential, a double power law, and a piecewise function with breaks at physically motivated ages. We find that the average attenuation law of the sample changes slightly based on the SFH assumed. Specifically, the exponential SFH leads to the shallowest attenuation curves. Using simulated data, we also find the exponential SFH fails to outperform the more complex SFHs. Finally, we find a systematic offset in the derived bump strength between SED fits with and without UVOT data, where the inclusion of UVOT data leads to smaller bump strengths, highlighting the importance of the UVOT data. This discrepancy is not seen in fits to mock photometry. Understanding dust attenuation in the local universe is key to understanding high-redshift objects where rest-frame far-infrared data are unavailable.

LANDSAT 8 LST Pan sharpening using novel principal component based downscaling model

The panchromatic (PAN) band is accessible at a much higher spatial resolution than the thermal infrared (TIR) band in remote sensing satellites. To mitigate this issue, current research proposes a fusion method based on principal components of PAN and TIR band pairs to enhance the spatial resolution of Land surface temperature (LST) images. In this research, LANDSAT-8 LST images of 100 m resolution have been downscaled to 15 m resolution by the Novel principal component (NPC) based downscaling model. The downscaled LST image's thermal values have been cross-verified by in situ thermal points collected by the thermal data logger and image quality indices. The results have shown the root mean square error (RMSE) of 0.51–0.92 for downscaled LST image from 100m to 15m resolution. The correlation between downscaled LST and ground truth points is between 0.54 and 0.83. The proposed NPC-based LST-PAN combination technique collects spatial details of the Earth's surface from PAN data as well as indulges thermal information of TIR image. The proposed fusion technique has upgraded the spatial goal and peculiarity of TIR images data set. The higher spatial resolution of thermal images can increase the level of precision in various remote sensing applications. These improved data sets can be used in a variety of applications, including urban heat island mapping, agricultural crop monitoring, forest fire detection, and hydrological modeling, to name a few. The increased spatial resolution provided by NPC fusion results in more accurate and consistent temperature readings, leading to improved decision-making and management in these and other applications.

Gangotri glacier dynamics from multi-sensor SAR and optical data

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese’s Advance Land Observation System (ALOS) PALSAR-2 SAR data for Spring of 2015 were used to derive glacier-surface velocity at seasonal time scale using Differential InSAR (DInSAR) techniques. Bi-static TanDEM-X (Experimental) data was used for the 1st time to estimate glacier surface elevation changes for a period of 22, 44, 88 days during summer of 2012 using InSAR techniques in this study. Annual glacier velocity was also estimated using temporal panchromatic data of LANDSAT-5 (30 m), LANDSAT-7/8 (15 m), Sentinel-2 (10 m) and Indian Remote Sensing Satellite IRS-1C/1D panchromatic (5 m) data during 1998–2019 with feature tracking approach. This study has estimated glacier surface velocity and surface elevation changes for the major parts of Gangotri glacier and its tributary glaciers using medium to high resolution optical and SAR datasets, at annual and seasonal time scale, which is an improvement over earlier studies, wherein snout based glacier recession or only main glacier velocities were reported. The velocity and slope were used to assess glacier-ice thickness distribution using Glabtop-2, slope dependent and laminar flow based methods over the Gangotri group of glaciers. The estimated ice thickness was estimated in the range of 58–550 m for the complete glacier while few small areas in middle & upper regions carry higher thickness of about 607 m. The estimated glacier-ice thickness was found in the range of 58–67 m at the snout region. The estimation was validated using 2014 field measurements from Terrestrial Laser Scanner (TLS) for the first time and correlation was found to be 0.799 at snout of the glacier.

P2Sharpen: A progressive pansharpening network with deep spectral transformation

Most existing deep learning-based methods for pansharpening task solely rely on the supervision of pseudo-ground-truth multi-spectral images, which exhibits two limitations in producing high-quality images. On the one hand, it is uncontrollable to regulate the full-resolution performance due to the fact that their whole training process only remain at the scale of reduced resolution. On the other hand, they ignore the accurate spatial information reference of high-resolution panchromatic images for supervision, resulting in insufficient spatial structure details. To address these challenges, we propose a progressive pansharpening network with deep spectral transformation, termed as P2Sharpen, where we balance the performance in different resolutions and make full use of the observed satellite data to improve the quality of fused results. First, we design a spectral transformation network (STNet) to cross the modality difference between multi-spectral data and panchromatic data, which establishes an accurate mapping function from MS to PAN images. Second, we propose a progressive pansharpening network (P2Net), in which the optimization of pansharpening at reduced and full resolutions is considered in a two-stage manner, balancing the performance at two scales effectively. In addition, we introduce the trained STNet to construct the consistency constraint between the sharpened result and PAN image at both reduced-resolution stage and full-resolution stage, which further improves the ability of P2Net for preserving spatial textures. Extensive experiments demonstrate that our method shows excellent performance over the state-of-the-arts on the sharpening quality and the spectral response consistency in both reduced and full resolutions. Moreover, the proposed method can be applied to generate the high-resolution normalized difference vegetation index with promising accuracy.

SKYSURF: Constraints on Zodiacal Light and Extragalactic Background Light through Panchromatic HST All-sky Surface-brightness Measurements. I. Survey Overview and Methods

We give an overview and describe the rationale, methods, and testing of the Hubble Space Telescope (HST) Archival Legacy project “SKYSURF.” SKYSURF uses HST’s unique capability as an absolute photometer to measure the ∼0.2–1.7 μm sky-surface brightness (sky-SB) from 249,861 WFPC2, ACS, and WFC3 exposures in ∼1400 independent HST fields. SKYSURF’s panchromatic data set is designed to constrain the discrete and diffuse UV to near-IR sky components: Zodiacal Light (ZL), Kuiper Belt Objects (KBOs), Diffuse Galactic Light (DGL), and the discrete plus diffuse Extragalactic Background Light (EBL). We outline SKYSURF’s methods to: (1) measure sky-SB levels between detected objects; (2) measure the discrete EBL, most of which comes from AB≃17–22 mag galaxies; and (3) estimate how much truly diffuse light may exist. Simulations of HST WFC3/IR images with known sky values and gradients, realistic cosmic ray (CR) distributions, and star plus galaxy counts were processed with nine different algorithms to measure the “Lowest Estimated Sky-SB” (LES) in each image between the discrete objects. The best algorithms recover the LES values within 0.2% when there are no image gradients, and within 0.2%–0.4% when there are 5%–10% gradients. We provide a proof of concept of our methods from the WFC3/IR F125W images, where any residual diffuse light that HST sees in excess of zodiacal model predictions does not depend on the total object flux that each image contains. This enables us to present our first SKYSURF results on diffuse light in Carleton et al.

Machine Learning in Pansharpening: A benchmark, from shallow to deep networks

Machine learning (ML) is influencing the literature in several research fields, often through state-of-the-art approaches. In the past several years, ML has been explored for pansharpening, i.e., an image fusion technique based on the combination of a multispectral (MS) image, which is characterized by its medium/low spatial resolution, and higher-spatial-resolution panchromatic (PAN) data. Thus, ML for pansharpening represents an emerging research line that deserves further investigation. In this article, we go through some powerful and widely used ML-based approaches for pansharpening that have been recently proposed in the related literature. Eight approaches are extensively compared. Implementations of these eight methods, exploiting a common software platform and ML library, are developed for comparison purposes. The ML framework for pansharpening will be freely distributed to the scientific community. Experimental results using data acquired by five commonly used sensors for pansharpening and well-established protocols for performance assessment (both at reduced resolution and at full resolution) are shown. The ML-based approaches are compared with a benchmark consisting of classical and variational optimization (VO)-based methods. The pros and cons of each pansharpening technique, based on the training-by-examples philosophy, are reported together with a broad computational analysis. The toolbox is provided in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liangjiandeng/DLPan-Toolbox</uri> .

COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys

We present a novel method for estimating galaxy physical properties from spectral energy distributions (SEDs) as an alternative to template fitting techniques and based on self-organizing maps (SOMs) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has previously been tested with hydrodynamical simulations in Davidzon et al. (2019, MNRAS, 489, 4817), however, here it is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high-quality panchromatic data set, thus we selected “COSMOS2020” galaxy catalog for this purpose. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOMs to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities resulted in a good agreement with independent measurements derived from more extended photometric baseline and, in addition, their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star-forming galaxies that is consistent with the findings of previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of replacing the usual synthetic templates with a collection of empirical SEDs built by the SOM in a “data-driven” way. Such an approach also allows, even for extremely large data sets, for an efficient visual inspection to identify photometric errors or peculiar galaxy types. While also considering the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys such as Euclid of the Legacy Survey of Space and Time at the Vera C. Rubin Telescope.

Parallelized Nonlinear Target Detection for Asbestos Identification in Large-Scale Remote Sensing Data

Due to the side effects of asbestos on human health and environments, many countries have banned the use of asbestos-containing materials, but there are still illegal products with asbestos in daily life. In order to investigate the distributions of asbestos to facilitate its removal, this paper studies the feasibility of asbestos identification with HyperSpectral (HS) and panchromatic (PAN) data, taking images captured by the PRISMA and ZY1E 2D satellites over Pavia, Italy as examples. In this work, a pansharpening method with guided filter was used to improve HS image quality in terms of spectral fidelity and spatial details. Then, the possible location of asbestos could be obtained by a nonlinear target detector named BSTD. Considering high computational cost for large-scale remote sensing data processing, we further develop BSTD to its parallelized version (denoted as PBSTD). Given the groundtruth of asbestos over Pavia by the Regional Environmental Protection Agency-ARPA Lombardia, our PBSTD and several popular methods are evaluated from both qualitative and quantitative perspectives, showing that most algorithms could correctly detect large-size asbestos roofs, and the nonlinear PBSTD and MSDinter perform better in small-size asbestos identification than other linear detectors. However, the detection accuracy on small-size asbestos is insufficient in practical applications, which indicates that there are still issues to achieve accurate small-size asbestos identification using coarse-spatial-resolution spaceborne remote sensing.

Remote Sensing Image Land Classification Based on Deep Learning

Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed. Firstly, the remote sensing image obtained by Gaofen-1 satellite is preprocessed, including multispectral data and panchromatic data. Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realize the fusion of remote sensing image features. Finally, the fused image features are input into the trained depth belief network (DBN) for processing, and the land type is obtained by the Softmax classifier. Based on the Keras and TensorFlow platform, the experimental analysis of the proposed model shows that it can clearly classify all land types, and the overall accuracy, F1 value, and reasoning time of the classification results are 97.86%, 87.25%, and 128 ms, respectively, which are better than other comparative models.

QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.

A fast radiometric correction method for Sentinel-2 satellite images

Purpose Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration. Design/methodology/approach The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation. Findings This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable. Originality/value For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

Active galactic nuclei catalog from the AKARI NEP-Wide field

Context. The north ecliptic pole (NEP) field provides a unique set of panchromatic data that are well suited for active galactic nuclei (AGN) studies. The selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such methods, despite their effectiveness, strongly reduce the breadth of resulting catalogs due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data. Aims. The aim of this study is to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique. Methods. We tested set of supervised machine learning algorithms for the purposes of carrying out an efficient process for AGN selection. The best models were compiled into a majority voting scheme, which used the most popular classification results to produce the final AGN catalog. An additional analysis of the catalog properties was performed as a spectral energy distribution fitting via the CIGALE software. Results. The obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized by 73% purity and 64% completeness. This new classification demonstrates a suitable consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found solely using the new method due to the lack of MIR detection for most of the new AGN candidates. The training data, codes, and final catalog are available via the github repository. The final catalog of AGN candidates is also available via the CDS service. Conclusions. The new selection methods presented in this paper are proven to be a better alternative for the MIR color AGN selection. Machine learning techniques not only show similar effectiveness, but also involve less demanding optical and NIR observations, substantially increasing the extent of available data samples.

Spatio-temporal change delineation and forecasting of snow/ice-covered areas of Sikkim Himalaya using multispectral and thermal band combinations of landsat imagery

The widespread retreat of snow/ice-covered areas from the past century has given rise to the sea level. The snow/ice-covered area of Sikkim Himalaya is not an exception. Snow-covered areas are disappearing at an alarming rate, which would give rise to water scarcity especially to the population dwelling in the mountainous regions. Changes in the snow/ice-covered areas indicate a huge threat to the changing climate. Therefore, snow cover area monitoring is currently an important aspect of climate change studies. Landsat data are widely accepted and immensely valued for snow cover mapping. An assessment through change delineation has been performed for the images acquired during the accumulation period of snow for the years 1998, 2008, and 2018 and another three acquired during the ablation period for the years 2000, 2009, and 2017. Snow Cover Index (S3) and Normalized Difference Snow Thermal Index (NDSTI) techniques have been applied to the images. A newly proposed snow index called Perennial Snow Index (PSI) has been applied to delineate the perennial snow-covered regions using the difference of reflectance observed in a visible band (RED) and the Thermal infrared (TIR) band divided by the sum of the two reflectances of Landsat 8 after resampling to 15meter resolution through merging with panchromatic data. The acquired results demarcated the thick or perennial snow accumulated areal boundaries from the surrounding lesser snow deposited regions. The temporal change analysis of two decades shows a difference of 10% in the extent of the snow cover region in Sikkim Himalayas. S3 index values were taken into consideration while performing a Time series analysis. The observed values show a declining trend line along with forecasts till the year 2030. Global Land Ice Measurement from Space (GLIMS) program and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) data have been used for reference.

The Austrian node of the natural resources satellite remote sensing cloud service platform: examples of Sino-Austrian cooperation

ABSTRACT The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna, Austria. Under this agreement panchromatic and multispectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research. So far, nearly 500 GB of data have been uploaded to the server. This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies. Some of them are already completed, others are still ongoing. They include a geometric accuracy validation of ZY-3 data, an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos, and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria. An accuracy study of DTMs from ZY-3 stereo data, as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes

Hyperspectral PRISMA images are new and have not yet been evaluated for their ability to detect marine plastic litter. The hyperspectral PRISMA images have a fine spectral resolution, however, their spatial resolution is not high enough to enable the discrimination of small plastic objects in the ocean. Pansharpening with the panchromatic data enhances their spatial resolution and makes their detection capabilities a technological challenge. This study exploits, for the first time, the potential of using satellite hyperspectral data in detecting small-sized marine plastic litter. Controlled experiments with plastic targets of various sizes constructed from several materials have been conducted. The required pre-processing steps have been defined and 13 pansharpening methods have been applied and evaluated for their ability to spectrally discriminate plastics from water. Among them, the PCA-based substitution efficiently separates plastic spectra from water without producing duplicate edges, or pixelation. Plastic targets with size equivalent to 8% of the original hyperspectral image pixel coverage are easily detected. The same targets can also be observed in the panchromatic image, however, they cannot be detected solely by the panchromatic information as they are confused with other appearances. Exploiting spectra derived from the pan-sharpened hyperspectral images, an index combining methodology has been developed, which enables the detection of plastic objects. Although spectra of plastic materials present similarities with water spectra, some spectral characteristics can be utilized for producing marine plastic litter indexes. Based on these indexes, the index combining methodology has successfully detected the plastic targets and differentiated them from other materials.

Pan-Sharpening Based on Panchromatic Colorization Using WorldView-2

In order to overcome the lack of the multispectral image (MS) and adequately preserve the spatial information of panchromatic (PAN) image and the spectral information of MS image, this study proposes a method which adds the spectral information of the prior MS to the prior PAN during training, and only the posterior PAN is needed for predicting. Firstly, we introduce the autoencoder model based on image colorization and discuss its feasibility in the field of multi-band remote sensing image pan-sharpening. Then, the image quality evaluation functions including spatial and spectral indexes are formed as the loss function to control the image colorization model. Because the loss function contains spatial and spectral evaluation indexes, it could directly calculate the loss between the network output and the label considering characteristics of remote sensing images. Besides, the training data in our model is original PAN, this means that it is not necessary to make the simulated degraded MS and PAN data for training which is a big difference from most existing deep learning pan-sharpening methods. The new loss function including the spectral and spatial quality instead of the general MSE (mean square error), only the original PAN instead of the simulated degraded MS + PAN to be inputted, only the spectral feature instead of the direct fusion result to be learned, these three aspects change the current learning framework and optimization rule of deep learning pan-sharpening. Finally, thousands of remote sensing images from different scenes are adopted to make the training dataset to verify the effectiveness of the proposed method. In addition, we selected seven representative pan-sharpening algorithms and four widely recognized objective fusion metrics to evaluate and compare the performance on the WorldView-2 experimental data. The results show that the proposed method achieves optimal performance in terms of both the subjective visual effect and the object assessment.