Low-resolution Bands Research Articles

Hyperspectral Super-Resolution Reconstruction Network Based on Hybrid Convolution and Spectral Symmetry Preservation

Hyperspectral images (HSI) have high-dimensional and complex spectral characteristics, with dozens or even hundreds of bands covering the same area of pixels. The rich information of the ground objects makes hyperspectral images widely used in satellite remote sensing. Due to the limitations of remote sensing satellite sensors, hyperspectral images suffer from insufficient spatial resolution. Therefore, utilizing software algorithms to improve the spatial resolution of hyperspectral images has become an urgent problem that needs to be solved. The spatial information and spectral information of hyperspectral images are strongly correlated. If only the spatial resolution is improved, it often damages the spectral information. Inspired by the high correlation between spectral information in adjacent spectral bands of hyperspectral images, a hybrid convolution and spectral symmetry preservation network has been proposed for hyperspectral super-resolution reconstruction. This includes a model to integrate information from neighboring spectral bands to supplement target band feature information. The proposed model introduces flexible spatial-spectral symmetric 3D convolution in the network structure to extract low-resolution and neighboring band features. At the same time, a combination of deformable convolution and attention mechanisms is used to extract information from low-resolution bands. Finally, multiple bands are fused in the reconstruction module, and the high-resolution hyperspectral image containing global information is obtained by Fourier transform upsampling. Experiments were conducted on the indoor hyperspectral image dataset CAVE, the airborne hyperspectral dataset Pavia Center, and Chikusei. In the X2 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 46.335, 36.321, and 46.310, respectively. In the X4 super-resolution task, the PSNR values achieved on the CAVE, Pavia Center, and Chikusei datasets were 41.218, 30.377, and 38.365, respectively. The results show that our method outperforms many advanced algorithms in objective indicators such as PSNR and SSIM while maintaining the spectral characteristics of hyperspectral images.

The Differential Assembly History of the Centers and Outskirts of Main-sequence Galaxies at z ∼ 2.3

We present a study of spatially resolved star formation histories (SFHs) for 60 z ∼ 2.3 main-sequence, star-forming galaxies selected from the MOSDEF spectroscopic survey in the GOODS-N field, with median stellar mass = 9.75 and spanning the range . Photometry is decomposed into a central and an outer spatial component using observed z F850LP − H F160W colors. The Prospector code is used to model spectral energy distributions for the center, outskirt, and integrated galaxy using Hubble Space Telescope/ACS and WFC3, Spitzer/IRAC, and ground-based photometry, with additional constraints on gas-phase metallicity and spectroscopic redshift from MOSDEF spectroscopy. For the low-resolution bands, spatially resolved photometry is determined with an iterative approach. The reconstructed SFHs indicate that the majority of galaxies with are observed while their central regions undergo relatively recent (<100 Myr) bursts of star formation, whereas the outskirts have a smooth, quasi-steady SFH that gently increases toward the redshift of observation. The enhanced star formation activity of the central parts is broadly consistent with the idea that it is produced by highly dissipative gas compaction and accretion. The wide range of central densities and sizes observed in the sample suggests that, for the selected galaxies, such a process has started but is still far from being completed. The implication would be that selecting star-forming galaxies at cosmic noon frequently includes systems in an “evolved” evolutionary phase where the centers have recently started a burst of star formation activity that will likely initiate inside-out quenching in the next several hundred million years.

A CNN-Based Sentinel-2 Image Super-Resolution Method Using Multiobjective Training

Deep learning methods have become ubiquitous tools in many Earth observation applications, delivering state-of-the-art results while proving to generalize for a variety of scenarios. One such domain concerns the Sentinel-2 (S2) satellite mission, which provides multispectral images in the form of 13 spectral bands, captured at three different spatial resolutions: 10, 20, and 60 m. This research aims to provide a super-resolution mechanism based on fully convolutional neural networks (CNNs) for upsampling the low-resolution (LR) spectral bands of S2 up to 10-m spatial resolution. Our approach is centered on attaining good performance with respect to two main properties: consistency and synthesis. While the synthesis evaluation, also known as Wald’s protocol, has spoken for the performance of almost all previously introduced methods, the consistency property has been overlooked as a viable evaluation procedure. Recently introduced techniques make use of sensor’s modulation transfer function (MTF) to learn an approximate inverse mapping from LR to high-resolution images, which is on a direct path for achieving a good consistency value. To this end, we propose a multiobjective loss for training our architectures, including an MTF-based mechanism, a direct input–output mapping using synthetically degraded data, along with direct similarity measures between high-frequency details from already available 10-m bands, and super-resolved images. Experiments indicate that our method is able to achieve a good tradeoff between consistency and synthesis properties, along with competitive visual quality results.

Super-resolution of Sentinel-2 images using Wasserstein GAN

ABSTRACT The Sentinel-2 satellites deliver 13 band multi-spectral imagery with bands having 10 m, 20 m or 60 m spatial resolution. The low-resolution bands can be upsampled to match the high resolution bands to extract valuable information at higher spatial resolution. This paper presents a Wasserstein Generative Adversarial Network (WGAN) based approach named as DSen2-WGAN to super-resolve the low-resolution (i.e., 20 m and 60 m) bands of Sentinel-2 images to a spatial resolution of 10 m. A proposed generator is trained in an adversarial manner using the min-max game to super-resolve the low-resolution bands with the guidance of available high-resolution bands in an image. The performance evaluated using metrics such as Signal Reconstruction Error (SRE) and Root Mean Squared Error (RMSE) shows the effectiveness of the proposed approach as compared to the state-of-the-art method, DSen2 as the DSen2-WGAN reduced RMSE by 14.68% and 7%, while SRE improved by almost 4% and 1.6% for 6 and 2 super-resolution. Lastly, for further evaluation, we have used trained DSen2-WGAN model to super-resolve the bands of EuroSAT dataset, a satellite image classification dataset based on Sentinel-2 images. The per band classification accuracy of low-resolution bands shows significant improvement after super-resolution using our proposed approach.

A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution

Satellite-based remote sensing is important for monitoring the spatial distribution of water resources. The water index is currently one of the most widely used water body extraction methods. Based on Sentinel-2 remote sensing image, this study combines area-to-point regression kriging interpolation, bilinear interpolation, and the Gram–Schmidt (GS) pan-sharpening method with the water indices MNDWI, AWEIsh and WI2015 to compare different water body extraction methods. The experimental results showed that all water indices have satisfactory extraction ability, with the kappa coefficient as an accuracy threshold above 0.8. Moreover, the GS downscaling method combined with the WI2015 yielded the best performance. This research demonstrates the efficacy of the WI2015 method to extract water bodies in urban areas and its ability to comprehensively describe river water bodies. The findings indicate that high-resolution band information is particularly important for improving low-resolution band downscaling results and can significantly minimize erroneous water body extraction.

From Regression Based on Dynamic Filter Network to Pansharpening by Pixel-Dependent Spatial-Detail Injection

Compared with hardware upgrading, pansharpening is a low-cost way to acquire high-quality images, which usually combines multispectral images (MS) in low spatial resolution with panchromatic images (PAN) in high spatial resolution. This paper proposes a pixel-dependent spatial-detail injection network (PDSDNet). Based on a dynamic filter network, PDSDNet constructs nonlinear mapping of the simulated panchromatic band from low-resolution multispectral bands through filtering convolution regression. PDSDNet reduces the possibility of spectral distortion and enriches spatial details by improving the similarity between the simulated panchromatic band and the real panchromatic band. Moreover, PDSDNet assumes that if an ideal multispectral image that has the same resolution with the panchromatic image exists, each band of it should have the same spatial details as in the panchromatic image. Thus, the details we fill into each multispectral band are the same and they can be extracted effectively in one pass. Experimental results demonstrate that PDSDNet can generate high-quality fusion images with multispectral images and panchromatic images. Compared with BDSD, MTF-GLP-HPM-PP, and PanNet, which are widely applied on IKONOS, QuickBird, and WorldView-3 datasets, pansharpened images of the proposed method have rich spatial details and present superior visual effects without noticeable spectral and spatial distortion.

Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.

Hyperspectral Image Super-Resolution via Intrafusion Network

This article presents an intrafusion network (IFN) for hyperspectral image (HSI) super-resolution (SR). Given that the HSI is a 3-D data cube with both the spatial information and the spectral information, the key challenge to construct HSI SR is how to efficiently exploit the spectral information among consecutive low-resolution (LR) bands, besides the spatial information. The proposed IFN consists of three modules, including the spectral difference module, the parallel convolution module, and the intrafusion module, which directly utilizes both the spatial information and the spectral information for reconstructing the high-resolution HSI. Different from most of the existed methods that tackle the spatial and spectral information separately, the proposed spatial–spectral utilization is achieved in one integrated network, which opens up a new way for HSI SR. Meanwhile, applications of this three modules strategy (first spectral difference, then parallel convolution, and finally, intrafusion) on both the conventional convolutional neural network and the residual network with deeper depth have shown the generalization capacity of this proposal. Experimental results and data analysis demonstrate the effectiveness of the proposed method using three hyperspectral data sets.

Sentinel-2 Sharpening via Parallel Residual Network

Sentinel-2 data is of great utility for a wide range of remote sensing applications due to its free access and fine spatial-temporal coverage. However, restricted by the hardware, only four bands of Sentinel-2 images are provided at 10 m resolution, while others are recorded at reduced resolution (i.e., 20 m or 60 m). In this paper, we propose a parallel residual network for Sentinel-2 sharpening termed SPRNet, to obtain the complete data at 10 m resolution. The proposed network aims to learn the mapping between the low-resolution (LR) bands and ideal high-resolution (HR) bands by three steps, including parallel spatial residual learning, spatial feature fusing and spectral feature mapping. First, rather than using the single branch network, the parallel residual learning structure is proposed to extract the spatial features from different resolution bands separately. Second, the spatial feature fusing is aimed to fully fuse the extracted features from each branch and produce the residual image with spatial information. Third, to keep spectral fidelity, the spectral feature mapping is utilized to directly propagate the spectral characteristics of LR bands to target HR bands. Without using extra training data, the proposed network is trained with the lower scale data synthesized from the observed Sentinel-2 data and applied to the original ones. The data at 10 m spatial resolution can be finally obtained by feeding the original 10 m, 20 m and 60 m bands to the trained SPRNet. Extensive experiments conducted on two datasets indicate that the proposed SPRNet obtains good results in the spatial fidelity and the spectral preservation. Compared with the competing approaches, the SPRNet increases the SRE by at least 1.538 dB on 20 m bands and 3.188 dB on 60 m bands while reduces the SAM by at least 0.282 on 20 m bands and 0.162 on 60 m bands.

Classification and Change Detection of Tirupati Urban Area using Erosion and Dilation Based PCA Transform

In remote sensing, the identification of the land use and land cover (LULC) changes in the global and local region are developed by classification and detection algorithms. This classification system can be developed to meet the needs of state agencies, and Federal for an up-to-date analyze of LULC throughout the entire selected of region area. The multispectral images have multiple low-resolution bands due to lack of sensory acquisition problem, haze-covered on earth objects and atmospheric distributions. So difficult to analyze the full information, the user wrongly interprets the information. Image processing applications can be done for compress and enhance the details of land surface details. The Principal Component Analysis and Morphological operations are implemented for compressing and feature extract the color and earth object values with good accuracy level. Change Detection between the time difference of the proposed enhanced images for land objects classes was computed. The most extensive land cover change category identification of the Tirupati urban Agricultural and forest area for the last 14 years. The change analyzed by using the image differencemethod for obtaining the changing level of the forest and urban development areas between two-timeintervals.

구조-텍스처 분할을 이용한 위성영상 융합 프레임워크

This paper proposes a novel framework for image fusion of satellite imagery to enhance spatial resolution of the image via structure-texture decomposition. The resolution of the satellite imagery depends on the sensors, for example, panchromatic images have high spatial resolution but only a single gray band whereas multi-spectral images have low spatial resolution but multiple bands. To enhance the spatial resolution of low-resolution images, such as multi-spectral or infrared images, the proposed framework combines the structures from the low-resolution image and the textures from the high-resolution image. To improve the spatial quality of structural edges, the structure image from the low-resolution image is guided filtered with the structure image from the high-resolution image as the guidance image. The combination step is performed by pixel-wise addition of the filtered structure image and the texture image. Quantitative and qualitative evaluation demonstrate the proposed method preserves spectral and spatial fidelity of input images.

Context adaptive panchromatic band simulation and detail injection for image pansharpening

General component substitution (CS) pansharpening methods establish a global model over the whole image plane and may lead to unexpected spectral distortion. This paper proposes a context adaptive CS pansharpening method, which features a totally local-based processing procedure. The method consists of two processing blocks. The first block is to simulate a low-resolution panchromatic band by a local linear regression model between panchromatic and multispectral bands. The second block extracts spatial details and adds details back to multispectral bands in locally varying ratios. By recasting the local linear regression model into the guided filtering framework and analyzing the implicit statistical assumptions underlying CS methods, the strengths of the local-based pansharpening algorithm are addressed. Experiments test 7 pairs of images acquired from different sensors, such as GF-2, Quickbird, and Worldview-2. Both quantitative and qualitative evaluations reveal that the presented method can better preserve the spectral information than some state-of-the-art methods.

A PAN-SHARPENING METHOD BASED ON GUIDED IMAGE FILTERING: A CASE STUDY OVER GF-2 IMAGERY

Abstract. The GaoFen-2 satellite (GF-2) is a self-developed civil optical remote sensing satellite of China, which is also the first satellite with the resolution of being superior to 1 meter in China. In this paper, we propose a pan-sharpening method based on guided image filtering, apply it to the GF-2 images and compare the performance to state-of-the-art methods. Firstly, a simulated low-resolution panchromatic band is yielded; thereafter, the resampled multispectral image is taken as the guidance image to filter the simulated low resolution panchromatic Pan image, and extracting the spatial information from the original Pan image; finally, the pan-sharpened result is synthesized by injecting the spatial details into each band of the resampled MS image according to proper weights. Three groups of GF-2 images acquired from water body, urban and cropland areas have been selected for assessments. Four evaluation metrics are employed for quantitative assessment. The experimental results show that, for GF-2 imagery acquired over different scenes, the proposed method can not only achieve high spectral fidelity, but also enhance the spatial details

An Application of Multi-band Forced Photometry to One Square Degree of SERVS: Accurate Photometric Redshifts and Implications for Future Science

Abstract We apply The Tractor image modeling code to improve upon existing multi-band photometry for the Spitzer Extragalactic Representative Volume Survey (SERVS). SERVS consists of post-cryogenic Spitzer observations at 3.6 and 4.5 μm over five well-studied deep fields spanning 18 deg2. In concert with data from ground-based near-infrared (NIR) and optical surveys, SERVS aims to provide a census of the properties of massive galaxies out to z ≈ 5. To accomplish this, we are using The Tractor to perform “forced photometry.” This technique employs prior measurements of source positions and surface brightness profiles from a high-resolution fiducial band from the VISTA Deep Extragalactic Observations survey to model and fit the fluxes at lower-resolution bands. We discuss our implementation of The Tractor over a square-degree test region within the XMM Large Scale Structure field with deep imaging in 12 NIR/optical bands. Our new multi-band source catalogs offer a number of advantages over traditional position-matched catalogs, including (1) consistent source cross-identification between bands, (2) de-blending of sources that are clearly resolved in the fiducial band but blended in the lower resolution SERVS data, (3) a higher source detection fraction in each band, (4) a larger number of candidate galaxies in the redshift range 5 &lt; z &lt; 6, and (5) a statistically significant improvement in the photometric redshift accuracy as evidenced by the significant decrease in the fraction of outliers compared to spectroscopic redshifts. Thus, forced photometry using The Tractor offers a means of improving the accuracy of multi-band extragalactic surveys designed for galaxy evolution studies. We will extend our application of this technique to the full SERVS footprint in the future.

Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels

A new resolution enhancement method is presented for multispectral and multiresolution images, such as those provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the subpixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.

PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2

In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

PANSHARPENING ON THE NARROW VNIR AND SWIR SPECTRAL BANDS OF SENTINEL-2

Abstract. In this paper results from the evaluation of several state-of-the-art pansharpening techniques are presented for the VNIR and SWIR bands of Sentinel-2. A procedure for the pansharpening is also proposed which aims at respecting the closest spectral similarities between the higher and lower resolution bands. The evaluation included 21 different fusion algorithms and three evaluation frameworks based both on standard quantitative image similarity indexes and qualitative evaluation from remote sensing experts. The overall analysis of the evaluation results indicated that remote sensing experts disagreed with the outcomes and method ranking from the quantitative assessment. The employed image quality similarity indexes and quantitative evaluation framework based on both high and reduced resolution data from the literature didn’t manage to highlight/evaluate mainly the spatial information that was injected to the lower resolution images. Regarding the SWIR bands none of the methods managed to deliver significantly better results than a standard bicubic interpolation on the original low resolution bands.

Wavelet and Curvelet Transformation based Image Fusion with ANFIS and SVM

The Image fusion is a data fusion innovation which keeps images as main research substance which refers to the strategies that integrate multi-images of the same scene from multiple image sensor data or integrate multi images of the same scene at different times from single image sensor. In this paper we describes a novel image fusion method, is suitable for pan-sharpening of multispectral (MS) bands which are based on multi-resolution analysis. The low-resolution MS bands are sharpened by injecting high-pass directional details extracted from the high-resolution panchromatic (Pan) image by means of the Wavelet and Curvelet transform, which is a non-separable MRA, whose basis function are directional edges with progressively increasing resolution. We introduce a new method based on the Wavelet and Curvelet transform using Neural Network which represents edges better than wavelets in this paper. Therefore, edges play a fundamental role in image understanding and one important way to enhance spatial resolution is to enhance the edges. Wavelet and Curvelet-based image fusion method provides richer information in the spatial and spectral domains simultaneously General Terms Image Fusion, Image processing, pattern recognition

Formula omitted] Jahn–Teller effect in the [formula omitted] cation and its signatures in the photoelectron spectrum of P4

The Jahn–Teller effect in the electronic ground state of the P4+ radical cation, which is one of the strongest E×e Jahn–Teller effects known in nature, has been revisited in this work with computational methods. The relevance of the Jahn–Teller coupling terms beyond second order in normal-mode displacements has been investigated. An elegant and efficient scheme based on polynomial invariant theory has been employed to expand the E×e potential energy matrix up to arbitrarily high orders in normal mode displacements. Using the state-averaged complete-active-space self-consistent-field method and a correlation consistent double-ζ basis set, an accurate ab initio adiabatic E×e Jahn–Teller potential-energy surface was obtained. It is shown that a polynomial expansion of least up to sixth order is necessary to account for the pronounced anharmonicity of the ab initio potential-energy surface for large amplitude displacements of the Jahn–Teller active vibrational mode. The vibronic structure of the X̃2E band of the photoelectron spectrum of P4 has been computed using a time-dependent wave-packet propagation method. The results reveal the significance of the higher-order Jahn–Teller coupling terms for the high-resolution vibronic spectrum as well as for the low-resolution band shape.

The limits of rovibrational analysis: the severely entangled ν1 Polyad vibration of dichlorodifluoromethane in the greenhouse IR window.

Five intense bands of dichlorodifluoromethane (CFC-12, or R12) in the infrared atmospheric window help make it a major greenhouse contributor. These include the ν1 fundamental at 1101.4 cm(-1) and the ν2 + ν3 combination at 1128.6 cm(-1). High-resolution spectra measured using the Australian Synchrotron Far-Infrared beamline were analyzed, and transitions of C(35)Cl2F2 were assigned to ν1, ν2 + ν3, and the ν3 + 2ν5 combination at 1099.7 cm(-1). The (v3 = 1; v5 = 2) state couples indirectly to v1 = 1 via Fermi resonances linking both states with v2 = v3 = 1. The v1 = 1 rotational levels are further riddled with perturbations and avoided crossings due to Coriolis resonance with the upper vibrational states of ν2+ ν9 at 1102.4 cm(-1) and (indirectly) ν2 + ν7 at 1105.8 cm(-1). A global treatment of all these states fits the observed line positions and satisfactorily accounts for the significant intensity of ν2 + ν3. Spectral simulations elucidate resonance perturbations that affect the distribution of IR absorption in the CF stretch region, and consequently the global warming potential of R12. Combination levels derived from rovibrational analysis lead to reassessment of the gas phase wavenumber values for the ν3 (458.6 cm(-1)), ν7 (437.7 cm(-1)) and ν9 (436.9 cm(-1)) fundamentals of C(35)Cl2F2, consistent with a cold, vapor phase far IR spectrum and previously published solid state spectra. B3LYP and MP2 anharmonic frequency calculations provide further support. At the MP2/aug-cc-pVTZ level, the root mean square (r.m.s.) error for unscaled anharmonic fundamentals is 6.2 cm(-1), decreased to 1.7 cm(-1) if only considering the seven lowest wavenumber modes, and integrated band intensities according with experimental literature values. Smaller basis sets produce band strengths that are too high. Low-resolution band assignments are reported for C(35)Cl(37)ClF2, C(37)Cl2F2, and (13)C(35)Cl2F2.