High Spatial Spectral Resolution Research Articles

A spectral preserved model based on spectral contribution and dependence with detail injection for pansharpening

Pansharpening integrates the high spectral content of multispectral (MS) images and the fine spatial information of the corresponding panchromatic (PAN) images to produce a high spectral-spatial resolution image. Traditional pansharpening methods compensate for the spatial lack of the MS image using the PAN image details, which easily causes spectral distortion. To achieve spectral fidelity, a spectral preservation model based on spectral contribution and dempendence with detail injection for pansharpening is proposed. In the proposed model, first, an efficacy coefficient (CE) based on the spatial difference between the MS and PAN images is designed to suppress the impact of the detail injection on the spectra. Second, the spectral contribution and dependence (SCD) between the MS bands and pixels are considered to strengthen the internal adaptation of the spectra. Finally, a spectrally preserved model based on CE and SCD is designed to force the fused image fidelity in spectra when the MS image is pansharpened with the details of the PAN image. Experimental results show that the proposed model is effective.

Luojia-HSSR: A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

ABSTRACT High Spatial and Spectral Resolution (HSSR) remote-sensing images can provide rich spectral bands and detailed ground information, but there is a relative lack of research on this new type of remote-sensing data. Although there are already some HSSR datasets for deep learning model training and testing, the data volume of these datasets is small, resulting in low classification accuracy and weak generalization ability of the trained models. In this paper, an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China. To our knowledge, it is the largest HSSR dataset to date, with 6438 pairs of 256 × 256 sized samples (including 3480 pairs in the training set, 2209 pairs in the test set, and 749 pairs in the validation set), covering area of 161 km2 with spatial resolution 0.75 m, 249 Visible and Near-Infrared (VNIR) spectral bands, and corresponding to 23 classes of field-validated ground coverage. It is an ideal experimental data for spatial-spectral feature extraction. Furthermore, a new deep learning model 3D-HRNet for interpreting HSSR images is proposed. The conv-neck in HRNet is modified to better mine the spatial information of the images. Then, a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously. Subsequently, the 3D convolution is inserted into the HRNet to optimize the performance. The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union（FWIoU） reaching 80.54%, indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

Pansharpening scheme using spatial detail injection–based convolutional neural networks

Pansharpening produces a high spatial-spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi-resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA-based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye-1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.

Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation

The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.

High spectral and spatial resolved encryption and decryption of 3D color object based on holographic imaging spectroscopy

We propose a new method to secure 3D color object using the holographic imaging spectroscopy technique. First, the original 3D color object is illuminated with a super-continuum white laser source with no necessity to decompose 3D color object into RGB components in advance which is commonly a preprocessing operation in traditional monochromatic illuminated optical cryptosystem. The output spectrum of white laser can be used as an additional key. Then, we design an optical cryptosystem based on hyperspectral holography in order to encrypt both the spectral and spatial information simultaneously with a high spectral-spatial resolution. Finally, the decrypted images at multiple-wavelength are numerically averaged to significantly reduce the speckle noise which is hard to be eliminated in optical cryptosystems with monochromatic illumination. The feasibility of our proposal is validated with experimental hyperspectral images and numerical simulation.

Time–frequency analysis for delineating gas hydrates and free gas in the Mahanadi offshore, India

The present work demonstrates a case study in the Mahanadi offshore basin in the eastern margin of India, where gas hydrates have been delineated by seismic experiment, mainly by identifying a bottom simulating reflector or BSR on seismic section, based on its characteristic features. The BSR represents an acoustic impedance contrast between the high-velocity gas hydrates bearing sediments above and low-velocity gas-bearing or water-saturated sediments below. At some places along the continental margins of the world, the BSR has not been identified but gas hydrates were recovered by drilling, whereas at some other places BSR was identified but no gas hydrates were found. Thus, we need to find out an attribute that can be used to characterise gas hydrates and free gas reservoirs. The purpose of this work is to demonstrate this through spectral decomposition in the time–frequency domain. The study based on continuous wavelet transform (CWT) approach, which uses a mathematical mother wavelet, shows the illuminated zone with higher energy low-frequency anomaly, associated with free gas below the BSR. The study demonstrates that the CWT technique provides higher spectral-spatial resolution than that obtained by the short-term Fourier transform technique.

A pansharpening scheme using spectral graph wavelet transforms and convolutional neural networks

ABSTRACT The objective of the multispectral pansharpening scheme is to obtain high spatial-spectral resolution multispectral (MS) images using high spectral resolution MS and high spatial resolution panchromatic (Pan) images. Some distortions are found in the multiresolution analysis (MRA) based on pansharpening. It can be minimized by correct matching of the lowpass filter image. This paper illustrates the pansharpening approach that is based on multistage multichannel spectral graph wavelet transform and convolutional neural network (SGWT-PNN). In this scheme, the Pan image is decomposed by a multistage multichannel SGWT, and then a weighted combination of SGWT decomposition produces a lowpass filter component. Using the convolutional neural network model, this lowpass filter image is converted to a better-matched filter image which is completely fit to the MRA-based pansharpening methods. Simulation results in the context of qualitative and quantitative analysis demonstrates the effectiveness of the proposed scheme applied on datasets collected by different satellites.

A NEW SPECTRAL-SPATIAL SUBSPACE CLUSTERING ALGORITHM FOR HYPERSPECTRAL IMAGE ANALYSIS

Abstract. In the past decade, hyperspectral imaging techniques have been widely used in various applications to acquire high spectral-spatial resolution images from different objects and materials. Although hyperspectral images (HSIs) are useful tools to obtain valuable information from different materials, the processing of such data is challenging due to several reasons such as the high dimensionality and redundancy of the feature space. Therefore, advanced machine learning algorithms have been developed to analyse HSIs. Among the developed algorithms, unsupervised learning techniques have become popular since they are capable of processing HSIs without having prior knowledge. Generally, unsupervised learning algorithms analyse HSIs based on spectral information. However, in many applications, spatial information plays an eminent role, in particular when the input data is of high spatial resolution. In this study, we propose a new clustering approach by utilizing the sparse subspace-based concept within the hidden Markov random field (HMRF) structure to process HSIs in an unsupervised manner. The qualitative analyses of the obtained clustering results show that the proposed spectral-spatial clustering algorithm outperforms the sparse subspace-based clustering algorithm that only uses spectral information.

Nonlocal Low-Rank Abundance Prior for Compressive Spectral Image Fusion

Compressive spectral imaging (SI) (CSI) acquires few random projections of an SI reducing acquisition, storage, and, in some cases, processing costs. Then, this acquisition framework has been widely used in various tasks, such as target detection, video processing, and fusion. Particularly, compressive spectral image fusion (CSIF) aims at obtaining a high spatial-spectral resolution SI from two sets of compressed measurements: one from a hyperspectral image with a high-spectral low-spatial resolution, and one from a multispectral image with high-spatial low-spectral resolution. Most of the literature approaches include prior information, such as global low rank, smoothness, and sparsity, to solve the resulting ill-posed CSIF inverse problem. More recently, the high self-similarities exhibited by SIs have been successfully used to improve the performance of CSI inverse problems, including a nonlocal low-rank (NLLR) prior. However, to the best of our knowledge, this NLLR prior has not been implemented in the solution of the CSIF inverse problem. Therefore, this article formulates an approach that jointly includes the global low rank, the smoothness, and the NLLR priors to solve the CSIF inverse problem. The global low-rank prior is introduced with the linear mixture model that describes the SI as a linear combination of a set of few end-members to specific abundances. In this article, either the end-members are accurately estimated from the compressed measurements or initialized from a fast reconstruction of the hyperspectral image. Also, it assumes that the abundances preserve the smoothness and NLLR priors of the SI so that the fused image is obtained from the end-members and abundances that result when minimizing a cost function including the sum of two data fidelity terms and two regularizations: the smoothness and the NLLR. Simulations over three data sets show that the proposed approach increases the CSIF performance compared with literature approaches.

Classification of complex environments using pixel level fusion of satellite data

The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration

Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution hyperspectral image (HS). In this paper, a new fusion method is proposed to improve the fusion performance by taking further advantage of the distribution characteristics of ground objects. First, we put forward a local adaptive sparse unmixing based fusion (LASUF) algorithm, in which the sparsity of the abundance matrices is appended as the constraint to the optimization fusion, considering the limited categories of ground objects in a specific range and the local correlation of their distribution. Then, to correct the possible original subpixel misregistrations or those introduced by the fusion procedures, a subpixel calibration method based on optimal matching adaptive morphology filtering (OM-AMF) is designed. Experiments on various datasets captured by different sensors demonstrate that the proposed fusion algorithm surpasses other typical fusion techniques in both spatial and spectral domains. The proposed method effectively preserves the spectral composition features of the isolated ground objects within a small area. In addition, the OM-AMF postprocessing is able to spatially correct the fusion results at a subpixel level and preserve the spectral features simultaneously.

An Ecological Land Cover Sampling Reclassification Model for Safety Estimation of Shoreline Systems from a Flood Defense Perspective Using Optical Satellite Remote Sensing Imaging

The safety level of a shoreline is essential for flood control projects and policy formulation or modification from both economic and environmental perspectives. With the development of remote sensing (RS) techniques, high spatial-spectral resolution and quick-revolution satellite images are now available and widely used in environment monitoring and management. It is therefore possible to more efficiently and conveniently identify the components of, and extract information for, shoreline environments. However, the problem is that the shoreline is always a long curve with a relatively narrow width, which limits the application of RS technology. This paper presents a method of recognizing different types of shoreline and of conveniently extracting the geographical coordinates of potential shoreline defense by analyzing and processing ecological information from an optical satellite RS data interpretation of land cover on both side of the shoreline. An application of this model in a low-resolution image case proved that the model can be used in the primary survey of a shoreline monitoring service platform as the basic tile level. The classification model is designed such that the requirements of image resolution for efficiently extracting information from the shoreline are low and the limitations imposed by a narrow shoreline width are avoided.

Bayer Filter Snapshot Hyperspectral Fundus Camera for Human Retinal Imaging

ABSTRACTPurpose: To demonstrate the versatility and performance of a compact Bayer filter snapshot hyperspectral fundus camera for in-vivo clinical applications including retinal oximetry and macular pigment optical density measurements.Methods: 12 healthy volunteers were recruited under an Institutional Review Board (IRB) approved protocol. Fundus images were taken with a custom hyperspectral camera with a spectral range of 460–630 nm. We determined retinal vascular oxygen saturation (sO2) for the healthy population using the captured spectra by least squares curve fitting. Additionally, macular pigment optical density was localized and visualized using multispectral reflectometry from selected wavelengths.Results: We successfully determined the mean sO2 of arteries and veins of each subject (ages 21–80) with excellent intrasubject repeatability (1.4% standard deviation). The mean arterial sO2 for all subjects was 90.9% ± 2.5%, whereas the mean venous sO2 for all subjects was 64.5% ± 3.5%. The mean artery–vein (A–V) difference in sO2 varied between 20.5% and 31.9%. In addition, we were able to reveal and quantify macular pigment optical density.Conclusions: We demonstrated a single imaging tool capable of oxygen saturation and macular pigment density measurements in vivo. The unique combination of broad spectral range, high spectral–spatial resolution, rapid and robust imaging capability, and compact design make this system a valuable tool for multifunction spectral imaging that can be easily performed in a clinic setting.

Side detection of strong radiation-mode out-coupling from blazed FBGs in single-mode and multimode fibers

We report on the effective side detection of radiation-mode out-coupling from blazed fiber Bragg gratings (BFBGs) fabricated in single-mode fiber (SMF) and multimode fiber (MMF). The far-field radiation power distribution from BFBGs have been measured achieving a high spatial-spectral resolution (0.17 mm/nm). We have also investigated comparatively the transmission-loss characteristics of BFBGs in both fiber types, fabricated using phase-mask and holographic inscription techniques. Our results reveal clearly that the radiation out-coupling from BFBGs is significantly stronger and spectrally more confined in MMF than in SMF.