Radiometrically-Accurate Hyperspectral Data Sharpening

Improving the spatial resolution of hyperpsectral image (HSI) has traditionally been an important topic in the field of remote sensing. Many approaches have been proposed based on various theories including component substitution, multiresolution analysis, spectral unmixing, Bayesian probability, and tensor representation. However, these methods have some common disadvantages, such as that they are not robust to different up-scale ratios and they have little concern for the per-pixel radiometric accuracy of the sharpened image. Moreover, many learning-based methods have been proposed through decades of innovations, but most of them require a large set of training pairs, which is unpractical for many real problems. To solve these problems, we firstly proposed an unsupervised Laplacian Pyramid Fusion Network (LPFNet) to generate a radiometrically-accurate high-resolution HSI. First, with the low-resolution hyperspectral image (LR-HSI) and the high-resolution multispectral image (HR-MSI), the preliminary high-resolution hyperspectral image (HR-HSI) is calculated via linear regression. Next, the high-frequency details of the preliminary HR-HSI are estimated via the subtraction between it and the CNN-generated-blurry version. By injecting the details to the output of the generative CNN with the low-resolution hyperspectral image (LR-HSI) as input, the final HR-HSI is obtained. LPFNet is designed for fusing the LR-HSI and HR-MSI covers the same Visible-Near-Infrared (VNIR) bands, while the short-wave infrared (SWIR) bands of HSI are ignored. SWIR bands are equally important to VNIR bands, but their spatial details are more challenging to be enhanced because the HR-MSI, used to provide the spatial details in the fusion process, usually has no SWIR coverage or lower-spatial-resolution SWIR. To this end, we designed an unsupervised cascade fusion network (UCFNet) to sharpen the Vis-NIR-SWIR LR-HSI. First, the preliminary high-resolution VNIR hyperspectral image (HR-VNIR-HSI) is obtained with a conventional hyperspectral algorithm. Then, the HR-MSI, the preliminary HR-VNIR-HSI, and the LR-SWIR-HSI are passed to the generative convolutional neural network to produce an HR-HSI. In the training process, the cascade sharpening method is employed to improve stability. Furthermore, the self-supervising loss is introduced based on the cascade strategy to further improve the spectral accuracy. Experiments are conducted on both LPFNet and UCFNet with different datasets and up-scale ratios. Also, state-of-the-art baseline methods are implemented and compared with the proposed methods with different quantitative metrics. Results demonstrate that proposed methods outperform the competitors in all cases in terms of spectral and spatial accuracy

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions

Millimeter and submillimeter high angular resolution interferometric observations: dust in the heart of IRAS 18162-2048

The GGD27 complex includes the HH 80-81-80N system, which is one of the most powerful molecular outflows associated with a high mass star-forming region observed up to now. This outflow is powered by the star associated with the source IRAS 18162-2048. Here we report the detection of continuum emission at sub-arcsec/arcsec resolution with the Submillimeter Array at 1.36mm and 456microns, respectively. We detected dust emission arising from two compact cores, MM1 and MM2, separated by about 7" (~12000AU in projected distance). MM1 spatially coincides with the powerful thermal radio continuum jet that powers the very extended molecular outflow, while MM2 is associated with the protostar that drives the compact molecular outflow recently found in this region. High angular resolution obervations at 1.36mm show that MM1 is unresolved and that MM2 splits into two subcomponents separated by ~1". The mass of MM1 is about 4Msun and it has a size of <300AU. This is consistent with MM1 being associated with a massive and dense (n(H2)>10^9cm-3) circumstellar dusty disk surrounding a high-mass protostar, which has not developed yet a compact HII region. On the other hand, the masses of the two separate components of MM2 are about 2Msun each. One of these components is a compact core with an intermediate-mass young protostar inside and the other component is probably a pre-stellar core. MM1 is the brigthest source at 1.36mm, while MM2 dominates the emission at 456microns. These are the only (sub)millimeter sources detected in the SMA observations. Hence, it seems that both sources may contribute significantly to the bolometric luminosity of the region. Finally, we argue that the characteristics of these two sources indicate that MM2 is probably in an earlier evolutionary stage than MM1.Comment: Accepted in AJ (Oct 31, 2010

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

First LOFAR observations at very low frequencies of cluster-scale non-thermal emission: the case of Abell 2256

Abell 2256 is one of the best known examples of a galaxy cluster hosting large-scale diffuse radio emission that is unrelated to individual galaxies. It contains both a giant radio halo and a relic, as well as a number of head-tail sources and smaller diffuse steep-spectrum radio sources. The origin of radio halos and relics is still being debated, but over the last years it has become clear that the presence of these radio sources is closely related to galaxy cluster merger events. Here we present the results from the first LOFAR Low band antenna (LBA) observations of Abell 2256 between 18 and 67 MHz. To our knowledge, the image presented in this paper at 63 MHz is the deepest ever obtained at frequencies below 100 MHz in general. Both the radio halo and the giant relic are detected in the image at 63 MHz, and the diffuse radio emission remains visible at frequencies as low as 20 MHz. The observations confirm the presence of a previously claimed ultra-steep spectrum source to the west of the cluster center with a spectral index of -2.3 \pm 0.4 between 63 and 153 MHz. The steep spectrum suggests that this source is an old part of a head-tail radio source in the cluster. For the radio relic we find an integrated spectral index of -0.81 \pm 0.03, after removing the flux contribution from the other sources. This is relatively flat which could indicate that the efficiency of particle acceleration at the shock substantially changed in the last \sim 0.1 Gyr due to an increase of the shock Mach number. In an alternative scenario, particles are re-accelerated by some mechanism in the downstream region of the shock, resulting in the relatively flat integrated radio spectrum. In the radio halo region we find indications of low-frequency spectral steepening which may suggest that relativistic particles are accelerated in a rather inhomogeneous turbulent region.Comment: 13 pages, 13 figures, accepted for publication in A\&A on April 12, 201

VLA Radio Observations of the HST Frontier Fields Cluster Abell 2744: The Discovery of New Radio Relics

Cluster mergers leave distinct signatures in the ICM in the form of shocks and diffuse cluster radio sources that provide evidence for the acceleration of relativistic particles. However, the physics of particle acceleration in the ICM is still not fully understood. Here we present new 1-4 GHz Jansky Very Large Array (VLA) and archival Chandra observations of the HST Frontier Fields Cluster Abell 2744. In our new VLA images, we detect the previously known $\sim2.1$ Mpc radio halo and $\sim1.5$ Mpc radio relic. We carry out a radio spectral analysis from which we determine the relic's injection spectral index to be $\alpha_{\rm{inj}} = -1.12 \pm 0.19$. This corresponds to a shock Mach number of $\mathcal{M}$ = 2.05$^{+0.31}_{-0.19}$ under the assumption of diffusive shock acceleration. We also find evidence for spectral steepening in the post-shock region. We do not find evidence for a significant correlation between the radio halo's spectral index and ICM temperature. In addition, we observe three new polarized diffuse sources and determine two of these to be newly discovered giant radio relics. These two relics are located in the southeastern and northwestern outskirts of the cluster. The corresponding integrated spectral indices measure $-1.81 \pm 0.26$ and $-0.63 \pm 0.21$ for the SE and NW relics, respectively. From an X-ray surface brightness profile we also detect a possible density jump of $R=1.39^{+0.34}_{-0.22}$ co-located with the newly discovered SE relic. This density jump would correspond to a shock front Mach number of $\mathcal{M}=1.26^{+0.25}_{-0.15}$.Comment: accepted for publication in Ap