Search

A significant strategy for achieving a balance between spatial and spectral resolution is to combine hyperspectral remote sensing images with low spatial resolution and multispectral remote sensing images with high spatial resolution. For the issues of spectrum distortion and lack of the ability of gaining high frequency information when the Fuvar algorithm employs the Vertex Component Analysis (VCA) technique, this thesis presents a modified Fuvar (MFuvar) algorithm that utilizes the Maximum Distance Analysis (MDA) method instead of the VCA approach. The two subsets from the hyperspectral remote sensing image of GF-5 and the multispectral remote sensing image of Sentinel-2A, representing different land cover types were used as test data. The spectral fidelity and the ability of gaining high frequency information were assessed by using visual and statistical analysis. Fused images are compared with eight fusion methods, including SFIMHS, GLPHS, MAPSMM, CNMF, Hysure, SpaFusion, LTTR, and Fuvar, respectively. The results show that the MFuvar algorithm can keep the best balance between spectral fidelity and the ability of gaining high frequency information, and it is generally better than the compared eight algorithms. And it fulfils the automatic selection of end elements without manual intervention and increases the efficiency of algorithm operation. [ABSTRACT FROM AUTHOR]

Purpose: Hyperspectral imaging (HSI) collects detailed spectral information across hundreds of narrow bands, providing valuable datasets for applications such as medical diagnostics. However, the large size of HSI datasets, often exceeding several gigabytes, creates significant challenges in data transmission, storage, and processing. This study aims to develop a wavelet-based compression method that addresses these challenges while preserving the integrity and quality of spectral information., Approach: The proposed method applies wavelet transforms to the spectral dimension of hyperspectral data in three steps: 1) wavelet transformation for dimensionality reduction, 2) spectral cropping to eliminate low-intensity bands, and 3) scale matching to maintain accurate wavelength mapping. Daubechies wavelets were used to achieve up to 32x compression while ensuring spectral fidelity and spatial feature retention., Results: The wavelet-based method achieved up to 32x compression, corresponding to a 96.88% reduction in data size without significant loss of important data. Unlike PCA and ICA, the method preserved the original wavelength scale, enabling straightforward spectral interpretation. Additionally, the compressed data exhibited minimal loss in spatial features, with improvements in contrast and noise reduction compared to spectral binning., Conclusions: This study demonstrates that wavelet-based compression is an effective solution for managing large HSI datasets in medical imaging. The method preserves critical spectral and spatial information, and therefore facilitates efficient data storage and processing, providing the way for practical integration of HSI technology in clinical applications.

Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

Spatial context has been demonstrated to be effective to constrain sparse unmixing (SU) of hyperspectral images. However, the existing algorithms employed simple spatial information without keeping spectral fidelity. By considering the fact that adjacent pixels own not only the endmembers with same variations but also approximated fractional abundances, in this paper, local spectral similarity preserving (LSSP) constraint is proposed to preserve spectral similarity in a local area during robust sparse unmixing (RSU). Specially, four LSSP constraints are constructed using different-norm-constrained pixel-level difference over abundance-level difference in a local area. Moreover, a convex optimization algorithm is proposed to solve the proposed LSSP-constrained RSU (LSSP-RSU). Experimental results on both synthetic and real hyperspectral data demonstrate that the developed algorithms yield better values of the signal-to-reconstruction error (SRE). Especially, when using $l_{2}$ norm of pixel-level difference to weight the $l_{1}$ norm of abundance-level difference, the proposed LSSP-RSU algorithm can achieve superior unmixing performance. [ABSTRACT FROM AUTHOR]

This paper proposes an improved framelet-based pan-sharpening algorithm for Thailand Earth Observation System (THEOS) imagery to decrease the effects of different acquisition times between panchromatic (Pan) and multispectral (MS) images, in which the smoothing filter-based intensity modulation (SFIM) is introduced into low-frequency information fusion instead of the conventional "mean" rule. Moreover, a two-layer procedure is presented to reduce the impacts of mixed pixels caused by the large difference of spatial resolutions between the Pan and MS images. The proposed method is tested on two THEOS datasets and compared with the Gram–Schmidt, SFIM and traditional framelet-based methods. The portability across contourlet transform is also examined. Both qualitative and quantitative evaluation results demonstrate that the proposed method is more independent of the illumination of the Pan image and can achieve better spectral fidelity while maintaining spatial sharpness. [ABSTRACT FROM AUTHOR]

Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.

With nanometric spectral resolution and number of bands as high as 220, Hyper spectral sensors like Hyperion and AVIRIS are gaining wide appreciation. Narrow, continuous wavelength of bands upon a vast spectrum of electromagnetic wavelength enables better precision in identification of materials by distinguishing between their unique spectral signatures. However, their poor spatial resolution is a major impediment in realising the full potential of hyperspectral imaging. Efforts are being made worldwide to improve the spatial resolution of hyperspectral imagery by fusing them with registered panchromatic imagery of higher resolution. However, most of the conventional methods fail to preserve the spectral fidelity of the fused images due to severe color distortion. Here, we propose an efficient paradigm to sharpen hyperspectral imagery with high spatial information content and minimal color distortion using color normalization by Lαβ and intensity image generation using Spectral Mixture Analysis. Quantitative assessment indices have been calculated to prove that our method is superior in terms of minimization of color distortion and maximization of spatial details as compared to other methods. [ABSTRACT FROM AUTHOR]

Variational pansharpening can enhance the spatial resolution of a hyperspectral (HS) image using a high-resolution panchromatic (PAN) image. However, this technology may lead to spectral distortion that obviously affect the accuracy of data analysis. In this article, we propose an improved variational method for HS image pansharpening with the constraint of spectral difference minimization. We extend the energy function of the classic variational pansharpening method by adding a new spectral fidelity term. This fidelity term is designed following the definition of spectral angle mapper, which means that for every pixel, the spectral difference value of any two bands in the HS image is in equal proportion to that of the two corresponding bands in the pansharpened image. Gradient descent method is adopted to find the optimal solution of the modified energy function, and the pansharpened image can be reconstructed. Experimental results demonstrate that the constraint of spectral difference minimization is able to preserve the original spectral information well in HS images, and reduce the spectral distortion effectively. Compared to original variational method, our method performs better in both visual and quantitative evaluation, and achieves a good trade-off between spatial and spectral information. [ABSTRACT FROM AUTHOR]

Image pansharpening can generate a high-resolution hyperspectral (HS) image by combining a high-resolution panchromatic image and a HS image. In this paper, we propose a variational pansharpening method for HS imagery constrained by spectral shape and Gram⁻Schmidt (GS) transformation. The main novelties of the proposed method are the additional spectral and correlation fidelity terms. First, we design the spectral fidelity term, which utilizes the spectral shape feature of the neighboring pixels with a new weight distribution strategy to reduce spectral distortion caused by the change in spatial resolution. Second, we consider that the correlation fidelity term uses the result of GS adaptive (GSA) to constrain the correlation, thereby preventing the low correlation between the pansharpened image and the reference image. Then, the pansharpening is formulized as the minimization of a new energy function, whose solution is the pansharpened image. In comparative trials, the proposed method outperforms GSA, guided filter principal component analysis, modulation transfer function, smoothing filter-based intensity modulation, the classic and the band-decoupled variational methods. Compared with the classic variation pansharpening, our method decreases the spectral angle from 3.9795 to 3.2789, decreases the root-mean-square error from 309.6987 to 228.6753, and also increases the correlation coefficient from 0.9040 to 0.9367.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image fusion techniques integrate complimentary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and computer based processing tasks for extraction of detail information. As an important part of image fusion algorithms, pixel-level image fusion can combine spectral information of coarse resolution imagery with finer spatial resolution imagery. Ideally, the method used to merge data sets with high-spatial and highspectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper describes the Discrete Wavelet Transform (DWT) algorithm for the fusion of two images using different spectral transform methods and nearest neighbor resampling techniques. This research paper investigates the performance of fused image with high spatial resolution Cartosat-1(PAN) with LISS IV and Cartosat-1(PAN) sensor images with the LISS III sensor image of Indian Remote Sensing satellites. The visual and statistical analysis of fused images has shown that the DWT method outperforms in terms of Geometric, Radiometric, and Spectral fidelity. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs' number increases, the mosaicked image's geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity. [ABSTRACT FROM AUTHOR]

Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity. [ABSTRACT FROM AUTHOR]

Due to their advantages of having a wide bandwidth, low cost, and being easy to obtain, traditional photodetectors (PDs) are being widely applied in measurements of transient signals. The spatial inhomogeneity of such PD temporal responses was measured directly to account for the PD spatial effect of decay rate due to poor alignment in continuous wave cavity ringdown spectroscopy (CW-CRDS) experiments. Based on the measurements of three PDs (i.e., model 1611 (Newport), model 1811 (Newport), and model PDA10CF-EC (Thorlabs)), all the temporal responses followed a tendency of declining first and then rising, and steady platforms existed for the last two PDs. Moreover, as we expected, the closer the PD center was, the faster the response. On the other hand, the initial shut-off amplitude generally reached a larger value for a faster temporal response. As a result, the spatial effect can strongly influence the spectral line shape and value, which will introduce more errors into the precise measurements of spectral parameters using the CRDS technique if this effect is not considered. The defined effective detection area (EDA) of the PDs, which was close to the active area given by manufacturers, was the key parameter that should be paid more attention by researchers. Therefore, the PD should be aligned perfectly to make sure that the EDA covers the laser spot completely. [ABSTRACT FROM AUTHOR]

Image pansharpening can generate a high-resolution hyperspectral (HS) image by combining a high-resolution panchromatic image and a HS image. In this paper, we propose a variational pansharpening method for HS imagery constrained by spectral shape and Gram–Schmidt (GS) transformation. The main novelties of the proposed method are the additional spectral and correlation fidelity terms. First, we design the spectral fidelity term, which utilizes the spectral shape feature of the neighboring pixels with a new weight distribution strategy to reduce spectral distortion caused by the change in spatial resolution. Second, we consider that the correlation fidelity term uses the result of GS adaptive (GSA) to constrain the correlation, thereby preventing the low correlation between the pansharpened image and the reference image. Then, the pansharpening is formulized as the minimization of a new energy function, whose solution is the pansharpened image. In comparative trials, the proposed method outperforms GSA, guided filter principal component analysis, modulation transfer function, smoothing filter-based intensity modulation, the classic and the band-decoupled variational methods. Compared with the classic variation pansharpening, our method decreases the spectral angle from 3.9795 to 3.2789, decreases the root-mean-square error from 309.6987 to 228.6753, and also increases the correlation coefficient from 0.9040 to 0.9367. [ABSTRACT FROM AUTHOR]