Spectral Sampling Research Articles

A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery

Monitoring urban compositions spatially and temporally is a crucial issue for urban planning and management. Nowadays, remote sensing techniques have been widely applied for urban compositions extraction. Compared with other remote sensing techniques, spectral indices have significant advantages due to their parameter-free and easy implementation. However, existing indices cannot extract different urban compositions well, and some of them can only extract one composition with less attention to other urban compositions. In this study, based on the water- impervious surface-pervious surface (W-I-P) model, a novel urban composition index (UCI) was developed by analyzing the robust features from the global spectral samples. Additionally, a semi-empirical threshold of UCI was proposed to extract different urban compositions (water, impervious surface area and pervious surface area). Four cities of China were selected as study areas, Landsat-8 images and Google Earth images were used for quantitative analysis. Correlation analysis, separability analysis, and accuracy assessment were conducted among UCI and five other existed indices (single and multiple composition indices) at the urban and global scales. Results indicated that UCI had a stronger correlation with the ISA proportion and a higher separability between each urban composition. UCI also achieved the highest overall accuracy and Kappa coefficient in urban compositions extraction. The suggested semi-empirical threshold was also testified to be reliable and can be a reference for practical application. There is convincing evidence that UCI is a simple, efficient, and reliable index for urban compositions extraction.

Long-wave mid-infrared time-resolved dual-comb spectroscopy of short-lived intermediates.

In this Letter, an electro-optic dual-comb spectrometer with a central tunable range of 7.77-8.22 µm is demonstrated to perform transient absorption spectroscopy of the simplest Criegee intermediate (CH2OO), a short-lived species involved in many key atmospheric reactions, and its self-reaction product via comb-mode-resolved spectral sampling at microsecond temporal resolution. By combining with a Herriott-type flash photolysis cell, CH2OO can be probed with a detection limit down to ∼1×1011moleculescm-3. Moreover, pressure broadening of CH2OO absorption lines can be studied with spectrally interleaved dual-comb spectroscopy. This Letter holds promise for high-resolution precision measurements of transient molecules, especially for the study of large molecules in complex systems.

Infrared Observations of Ganymede From the Jovian InfraRed Auroral Mapper on Juno

AbstractThe Jovian InfraRed Auroral Mapper (JIRAM) on board the NASA Juno spacecraft is a dual‐band imager and spectrometer in the 2–5 μm range with 9‐nm spectral sampling, primarily designed to study the Jovian atmosphere and aurorae. In addition to these goals, JIRAM is used to obtain images and spectra of the Galilean satellites, every time the spacecraft attitude is favorable. Here we present JIRAM images and spectra of Ganymede obtained during the first 4 years of the mission. In particular, on 26 December 2019, during a relatively close passage of Juno with the moon, a dedicated reorientation of the spacecraft was performed to achieve optimized observations of Ganymede by Juno's remote sensing instruments, including JIRAM. In the outbound phase of the flyby, observing the northern polar regions of Ganymede at a distance of roughly 100,000 km, JIRAM collected infrared images and spectra of the surface at a spatial resolution as high as 23 km per pixel, covering high northern latitudes that were scarcely mapped previously. A photometric model of Ganymede reflectance is produced, which diverges from the Lambert model. The spatial distribution of the obtained spectra complements the available coverage of the surface, with particular regard to the 2.0‐µm water ice absorption band and, to a lesser extent, to the 4.26‐µm spectral feature diagnostic of CO2 trapped in water ice. The water ice distribution is compatible with sputtered‐induced water ice grain enrichment at high latitude (>45°). Several minor species (hydrated salts, trapped H2, CO2, and acids) are also identified in the measured spectra.

Experimental Investigation of the Acetylene–Benzene Cocrystal on Titan

Abstract Acetylene and benzene are two common molecules formed in Titan’s atmosphere, and are likely components of the lake evaporites. It is known that these two molecules can form a cocrystal, a molecule with a structure that is unique from that of the component molecules. Thus, we sought to study this cocrystal using an experimental setup that simulates Titan surface conditions (90 K, 1.5 bar). Using Fourier transform infrared (FTIR) spectroscopy, we characterize new spectral absorptions, band shifts, and morphological sample changes associated with this cocrystal from 1 to 2.6 μm, which overlaps with Cassini VIMS wavelength range (0.35–5.1 μm). This is the first study of the resulting acetylene–benzene cocrystal under Titan-relevant temperature and pressure. The cocrystal forms at 135 K and is stable down to 90 K. Our findings can be applied to the cocondensation process in Titan’s atmosphere, as well as the ongoing effort to better characterize the composition and spectral properties of Titan’s lake evaporites. These results can also provide a stepping stone to future surface missions such as Dragonfly, which will closely examine relevant surface materials on Titan.

Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate brown LAI (LAIB) next to green LAI (LAIG). By using LAI ground measurements from multiple campaigns associated with airborne or satellite spectra, Gaussian processes regression (GPR) models were developed for both LAIG and LAIB, providing alongside associated uncertainty estimates, which allows to mask out unreliable LAI retrievals with higher uncertainties. A processing chain was implemented to apply both models to S2 images, generating a multiband LAI product at 20 m spatial resolution. The models were adequately validated with in-situ data from various European study sites (LAIG: R2 = 0.7, RMSE = 0.67 m2/m2; LAIB: R2 = 0.62, RMSE = 0.43 m2/m2). Thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B12: 2190 nm) a distinction between LAIG and LAIB can be achieved. To demonstrate the capability of LAIB to identify when crops start senescing, S2 time series were processed over multiple European study sites and seasonal maps were produced, which show the onset of crop senescence after the green vegetation peak. Particularly, the LAIB product permits the detection of harvest (i.e., sudden drop in LAIB) and the determination of crop residues (i.e., remaining LAIB), although a better spectral sampling in the shortwave infrared would have been desirable to disentangle brown LAI from soil variability and its perturbing effects. Finally, a single total LAI product was created by merging LAIG and LAIB estimates, and then compared to the LAI derived from S2 L2B biophysical processor integrated in SNAP. The spatiotemporal analysis results confirmed the improvement of the proposed descriptors with respect to the standard SNAP LAI product accounting only for photosynthetically active green vegetation.

In-vitro spectrometric analysis of hyperlactatemia and lactic acidosis in buffer relating to sepsis

The normal range for pH in the human body is 7.35-7.45. When pH falls below 7.3, it is considered as severe acidemia. Acidemia, together with increased blood lactate concentrations (hyperlactatemia) constitute a severe threat to life, which is often referred to as lactic acidosis. The feasibility of near infrared transmission/reflectance spectroscopy as a tool to determine lactate concentration levels and pH, independently, has been well established. However, the effects on spectral features arising from simultaneous variations in pH and lactate are not fully understood. Hence, this paper reports on a spectroscopic study of 37 different lactate concentrations that were prepared at three different pH levels (7.4, 7.0 and 6.5). Near infrared spectra were acquired in the range 800-2500 nm, and were later divided into four spectral ranges. Further investigations were carried out on various wavelengths within each spectral range and sample set. Furthermore, partial least squares regression with cross-validation was performed on all data sets. The results showed a clear interdependence and overlapping spectral behavior between blood lactate concentrations and pH. The findings from this study suggest that for an accurate estimation of blood lactate using this technique, the pH of the sample must be previously known.

Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging

This research was conducted to examine the potential use of hyperspectral imaging for non-destructive detection of Grapevine leafroll-associated virus 3 (GLRaV-3) during asymptomatic and symptomatic stages of grapevine leafroll disease (GLD) in a red-berried wine grape (Vitis vinifera) cultivar. Cabernet Sauvignon vines tested positive and negative for GLRaV-3 were used in this study. Leaves from infected and non-infected vines were detached at five phenological stages and individual leaf images were acquired by a hyperspectral imager in 2017, 2018, and 2019 seasons. Those images were then preprocessed using spectra normalization and Monte-Carlo method for eliminating spectral sample outliers. Least absolute shrinkage and selection operator was used to select feature wavelengths for each phenological stage within three-season datasets. The sensitivity of selected feature wavelengths was evaluated based on analysis of variance and linear regression. Six salient wavelengths (690, 715, 731, 1409, 1425 and 1582 nm) were determined as sensitive wavebands for detecting virus symptoms in leaf samples. The detectability of GLD using those six salient wavelengths was evaluated using least squares-support vector machine. The classification accuracy was found between 66.67 and 89.93% for test datasets collected at first asymptomatic stage over three seasons. These results indicated that the hyperspectral imaging technique has the potential for nondestructively detecting virus-infected grapevines during asymptomatic stages.

Coexisting Infinite Orbits in an Area-Preserving Lozi Map.

Extreme multistability with coexisting infinite orbits has been reported in many continuous memristor-based dynamical circuits and systems, but rarely in discrete dynamical systems. This paper reports the finding of initial values-related coexisting infinite orbits in an area-preserving Lozi map under specific parameter settings. We use the bifurcation diagram and phase orbit diagram to disclose the coexisting infinite orbits that include period, quasi-period and chaos with different types and topologies, and we employ the spectral entropy and sample entropy to depict the initial values-related complexity. Finally, a microprocessor-based hardware platform is developed to acquire four sets of four-channel voltage sequences by switching the initial values. The results show that the area-preserving Lozi map displays coexisting infinite orbits with complicated complexity distributions, which heavily rely on its initial values.

Linking domain knowledge and machine interpretation using braid theory and cognitive sampling: Applications to underwater acoustics, and machine interpretation of melodic structures in North Indian classical music

Learning domain-interpretable features unbiased by model uncertainties is challenging due to training bias, incomplete model knowledge and human subjectivity. It is challenging to solve this dilemma due to limitations of available training data, incomplete or conflicting models, as well as lack of integrational framework that resolves model inconsistencies to build an unbiased feature dictionary. We introduce a novel sampling strategy called cognitive sampling that allows combining domain knowledge and machine interpretation to influence spectral sampling using geometric constructs such as braid, knots and links within the dataset, and thus enhance the accuracy of machine classification beyond so-called “black box” techniques. Beyond representative results from traditional applications such as underwater acoustics, we will also explore machine-driven music cognition based on melodic structures (Raags) from North Indian classical music. The Raag tradition exhibits intricate yet improvised melodic structure that cannot be easily quantified in Western staff notation due to its fluid forms and presence of microtones. Recent efforts to incorporate learning networks for machine-driven melodic interpretation have been met with partial success, the primary limitation being dearth of expert-interpretable feature engineering. Presented results will be based on underwater acoustic datasets and sarod recordings performed by Pt. Tejendra Narayan Majumdar.

Convolutional neural network for preprocessing‐free bacterial Spectra identification

AbstractIdentifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is a tedious and labor‐intensive process. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real‐time identification without human experts, it still requires data preprocessing. To address this issue, we proposed a unified solution for the identification of bacterial species with a convolutional neural network. The neural network automatically determined species according to their mass spectra without the preprocessing steps. The convolutional and pooling layers in the neural network could replace the binning, baseline correction, and scaling procedures. Moreover, because of the explainable structure, the model could identify important regions of spectra to discriminate each bacterial species. We used spectral samples obtained from the fatty acid methyl esters of 10 samples from 16 bacterial species (a total of 16) to demonstrate the usefulness of the proposed method by comparing it with existing classification methods preceded by preprocessing. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. Moreover, the classification results of the proposed method are interpretable.

An evaluation of airborne SWIR imaging spectrometers for CH4 mapping: Implications of band positioning, spectral sampling and noise

The development of instruments and methods to assist in methane (CH4) emissions mapping is essential to the fossil fuel industry. Early identification of local CH4 sources can benefit both petroleum hydrocarbon prospecting and environmental monitoring. Generic airborne imaging spectrometers operating in the shortwave infrared (SWIR) wavelength range (1000−2400 nm) have shown their suitability for this task. However, to date, there is no airborne scanner specifically designed for the detection of CH4 plumes. To overcome current handicaps and achieve better results at local scale, further investigation in sensor design is needed to evaluate which components can be adjusted to improve CH4 mapping with airborne sensors. Here, we focus on the evaluation of currently operational airborne imaging spectrometers for CH4 mapping in the SWIR range. Data acquired over areas with known CH4 emissions by scientific and industry-grade airborne hyperspectral sensors were examined. The research was conducted in three steps: analysis of sensor design, image processing and noise simulation. In the first step, differences in the spectral sampling between the sensors were analyzed. A reference CH4 signature from HITRAN spectral database was resampled to the spectral sampling of each airborne sensor. The center wavelength of diagnostic CH4 absorption features (identified in the convolved signatures) in relation to the position of band centers from each equipment was examined. For the image processing stage, a new CH4 index and a classic matched filtering were used to map CH4 plumes. To assess the impact of the signal-to-noise ratio (SNR) of the airborne sensors on CH4 plume mapping, white noise was added to the data to simulate images with varying SNR levels. Results demonstrated that the wavelength position of band centers is a key for CH4 mapping. The CH4 plumes could be mapped only with scientific-grade sensors, in which the band centers were closer to the center of CH4 features. Simulations with the addition of random noise demonstrated that a noisier signal is probably the reason why the industry-grade sensor tested here failed to map CH4 plumes, given that all instruments have a comparable spectral sampling. Furthermore, the simulations also demonstrated that the density of the plume has also a weight on the mapping of CH4 sources, once the image that captured the densest plume requested a higher addition of noise to be lost. The overall investigation indicates that a hyperspectral airborne sensor with bands properly positioned and scientific-grade SNR would better resolve the narrow CH4 features in the SWIR range.

Modelling Reservoir Chlorophyll-a, TSS, and Turbidity Using Sentinel-2A MSI and Landsat-8 OLI Satellite Sensors with Empirical Multivariate Regression

Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with R2 of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with R2>0.9 from L8’s red, blue, and green bands, as compared to 0.47≤R2≥0.61 from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of R2>70% from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.

Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation

Visible and near-infrared (Vis-NIR) spectroscopy is a cost-effective technique for alternative soil physical and chemical analyses for estimating soil properties. The optimal band combination algorithm is an effective method of extracting spectral variables by considering the interaction information between wavebands, but for laboratory Vis-NIR spectral data, this method is susceptible to the “dimensional curse”. Here, we hypothesized that properly degrading the spectral configuration (i.e., decreasing the number of spectral bands and coarsening the spectral resolution) can improve the computational efficiency without affecting the prediction accuracy. To test this hypothesis, we constructed six degraded spectral configurations from an initial spectral database (i.e., consisting of 2001 spectral bands acquired with a portable ASD spectroradiometer) with a reduction in the number of spectral bands from 2001 to 19, a coarsened spectral resolution from 3 to 100 nm, and a spectral sampling interval equal to the spectral resolution (i.e., uniform interval sampling). In this study, the databases consisted of 255 soil samples collected from the Ebinur Lake area in Northwest China. The relationship between the soil organic matter (SOM) and the spectra was established using a partial least-squares-support vector machine (PLS-SVM) through two strategies: one is in accordance with the different salinity levels, and the other involves applying the optimal band combination algorithm from each spectral configuration. The results indicated that the soil salinity had a strong negative influence on the performance of the SOM models (R2cv, 0.46–0.81). However, the optimal band combination algorithm can improve the sensitivity (R2pre, 0.36–0.65) of spectral information and the SOM. Overall, the prediction accuracy obtained through the optimal band combination algorithm was generally superior to that from full-spectrum data. The prediction performance of the optimal band combination algorithm was accurate (R2pre ≥ 0.85) and stable (RPIQ pre, ~3.20), with a spectral resolution between 3 and 20 nm (i.e., the number of spectral bands decreased from 2001 to 99). Considering the accuracy and time-consuming nature of this approach, the combination of a 20 nm spectral resolution and an optimal band combination algorithm was the most effective method. In summary, this research will guide future studies in transforming hyperspectral datasets into parsimonious representations and uses the optimal band combination algorithm efficiently to determine the informative variable. Furthermore, the optimal band combination algorithm has broad application prospects in soil Vis-NIR spectroscopy and other fields of spectroscopy.

Spectro-temporal glimpsing of speech in noise: Regularity and coherence of masking patterns reduces uncertainty and increases intelligibility.

Adverse listening conditions involve glimpses of spectro-temporal speech information. This study investigated if the acoustic organization of the spectro-temporal masking pattern affects speech glimpsing in "checkerboard" noise. The regularity and coherence of the masking pattern was varied. Regularity was reduced by randomizing the spectral or temporal gating of the masking noise. Coherence involved the spectral alignment of frequency bands across time or the temporal alignment of gated onsets/offsets across frequency bands. Experiment 1 investigated the effect of spectral or temporal coherence. Experiment 2 investigated independent and combined factors of regularity and coherence. Performance was best in spectro-temporally modulated noise having larger glimpses. Generally, performance also improved as the regularity and coherence of masker fluctuations increased, with regularity having a stronger effect than coherence. An acoustic glimpsing model suggested that the effect of regularity (but not coherence) could be partially attributed to the availability of glimpses retained after energetic masking. Performance tended to be better with maskers that were spectrally coherent as compared to temporally coherent. Overall, performance was best when the spectro-temporal masking pattern imposed even spectral sampling and minimal temporal uncertainty, indicating that listeners use reliable masking patterns to aid in spectro-temporal speech glimpsing.

Ultrahigh-Resolution Direct-Frequency-Comb Spectrometer

High-resolution broadband spectroscopy is crucial for studying complex molecular structures and accurately retrieving the line shapes associated with their energy levels. These line shapes carry vital information about a molecule's thermodynamic properties such as temperature, pressure, and concentration, which are desirable for $e.g.$ the petroleum industry, environmental monitoring, and medical breath analysis. This study shows how to obtain a molecular spectrum with about 80 kHz of resolution and 7.86 MHz of spectral sampling. The promising high-resolution technique allows complete gas characterization by simultaneously measuring thermodynamic properties and analyzing composition.

Characterization of Background-Anomaly Separability With Generative Adversarial Network for Hyperspectral Anomaly Detection

Hyperspectral images (HSIs) have unique advantages in distinguishing subtle spectral differences of different materials. However, due to complex and diverse backgrounds, unknown prior knowledge, and imbalanced samples, it is challenging to separate background and anomaly. In this article, we present a novel characterization of background-anomaly separability with a generative adversarial network (BASGAN) for hyperspectral anomaly detection. The key contribution is the proposal to explicitly constrain the background and anomaly separability by characterizing background spectral samples while avoiding anomaly reconstruction. First, we use a class saliency map extraction algorithm to obtain pseudobackground and anomaly samples for adversarial training. To further mitigate the suffering of anomaly contamination in background distribution estimation, we introduce background-anomaly separability constrained loss function to enhance the reconstruction of the background while weakening the anomaly reconstruction in a semisupervised way. Additionally, a discriminator is induced into the latent space to make the encoded representation resemble Gaussian distribution during adversarial training. The other is adversarial training in the reconstruction space so that the background estimation can be improved. Experiments conducted on real data sets illustrate the superior background-anomaly separability of the proposed method.

Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing

Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is used for HLB rapid detection. A Cubert S185 (Airborne Hyperspectral camera) was mounted on the UAV of DJI Matrice 600 Pro to capture the hyperspectral remote sensing images; and a ASD Handheld2 (spectrometer) was used to verify the effectiveness of the remote sensing data. Correlation-proven UAV hyperspectral remote sensing data were used, and canopy spectral samples based on single pixels were extracted for processing and analysis. The feature bands extracted by the genetic algorithm (GA) of the improved selection operator were 468 nm, 504 nm, 512 nm, 516 nm, 528 nm, 536 nm, 632 nm, 680 nm, 688 nm, and 852 nm for the HLB detection. The proposed HLB detection methods (based on the multi-feature fusion of vegetation index) and canopy spectral feature parameters constructed (based on the feature band in stacked autoencoder (SAE) neural network) have a classification accuracy of 99.33% and a loss of 0.0783 for the training set, and a classification accuracy of 99.72% and a loss of 0.0585 for the validation set. This performance is higher than that based on the full-band AutoEncoder neural network. The field-testing results show that the model could effectively detect the HLB plants and output the distribution of the disease in the canopy, thus judging the plant disease level in a large area efficiently.

Optical fiber temperature sensor with insensitive refractive index and strain based on phase demodulation

AbstractWe propose and experimentally demonstrate a temperature sensor based on a suspended‐core microstructure optical fiber (SCMF). The sensor is constructed by fusion splicing a piece of SCMF between two up‐tapers which act as light beam couplers. The Mach‐Zehnder interference (MZI) is formed by the silica core modes and the air cladding modes in the SCMF. The sensitivity of −0.011 rad/°C in the temperature range of 20°C to 90°C is achieved, which is more accurate and stable than conventional wavelength tracking method due to the phase demodulation method can avoid the effects of spectral noise, dip selection, and wavelength sampling rate on the wavelength tracking. In addition, our proposed sensor is inert to external environments such as strain and refractive index, which can well solve the problem of crosstalk during temperature sensing.

STATUS OF THE IMAGING SPECTROSCOPY MISSION ENMAP WITH RADIOMETRIC CALIBRATION AND CORRECTION

Abstract. The high-resolution imaging spectroscopy remote sensing mission EnMAP (Environmental Mapping and Analysis Program, enmap.org) covers the spectral range from 420 nm to 2450 nm with a spectral sampling distance varying between 4.8 nm and 12.0 nm comprising 262 spectral bands. We focus on the planned framework concerning radiometry. The expected signal-to-noise ratio at reference radiance level is 500:1 at 495 nm and 150:1 at 2200 nm. The radiometric resolution is 14 bits and an absolute radiometric accuracy of better than 5% is achieved. Radiometric calibration is based on Sun calibration measurements with a fullaperture diffusor for absolute calibration. In addition, relative calibration monitors the instrument during the complete mission lifetime based on an integrating sphere (on the satellite). The fully-automatic on-ground image processing chain considers the derived radiometric calibration coefficients in the radiometric correction which is followed by the orthorectification and atmospheric compensation. Each of the two 2-dimensional detector arrays of the prism-based pushbroom dual-spectrometer works in a dual-gain configuration to cover the complete dynamic range. EnMAP will acquire 30 km in the across-track direction with a ground sampling distance of 30 m and the across-track tilt capability of 30° will enable a target revisit time of less than 4 days. The launch is scheduled for 2021. The high-quality products will be freely available to international scientific users for measuring and analysing diagnostic parameters which describe vital processes on the Earth’s surface.

Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection

Reliable detection of anomalies without any prior information is a critical yet challenging task in many applications, not least military and civilian fields. An intelligent anomaly detection system would use the material-specific spectral information in hyperspectral images (HSIs), thereby avoiding the loss of visually confusing objects. However, conventional hyperspectral anomaly detection methods are mainly achieved in an unsupervised way leading to limited performance due to lack of prior knowledge. In this article, we propose a novel autoencoder and adversarial-learning based semisupervised background estimation model (SBEM) that is trained only on the background spectral samples in order to accurately learn the background distribution. In particular, an unsupervised background searching method is firstly conducted on the original HSIs to search the background spectral samples. Our proposed SBEM consists of an encoder, a decoder, and a discriminator to thoroughly capture background distribution. Furthermore, jointly minimizing the reconstruction loss, spectral loss, and adversarial loss during training aids the model to learn the background distribution as required. Experiments on four real HSIs demonstrate that compared to the current state-of-the-art, the proposed framework yields higher detection capability and lower false alarm rate, which shows that it has a significant benefit in the tradeoff between detection accuracy and false alarm rate.