Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?
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ZY-1 02D is China’s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a swath width of 60 km. Its competitive advantages over other on-orbit or planned satellites are its high spectral resolution and large swath width. Unfortunately, the relatively low spatial resolution may limit its applications. As a result, fusing ZY-1 02D HS data with high-spatial-resolution multispectral (MS) data is required to improve spatial resolution while maintaining spectral fidelity. This paper conducted a comprehensive evaluation study on the fusion of ZY-1 02D HS data with ZY-1 02D MS data (10-m spatial resolution), based on visual interpretation and quantitative metrics. Datasets from Hebei, China, were used in this experiment, and the performances of six common data fusion methods, namely Gram-Schmidt (GS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Modified Intensity-Hue-Saturation (IHS), Wavelet Transform (Wavelet), and Color Normalized Sharping (Brovey), were compared. The experimental results show that: (1) HPF and GS methods are better suited for the fusion of ZY-1 02D HS Data and MS Data, (2) IHS and Brovey methods can well improve the spatial resolution of ZY-1 02D HS data but introduce spectral distortion, and (3) Wavelet and NND results have high spectral fidelity but poor spatial detail representation. The findings of this study could serve as a good reference for the practical application of ZY-1 02D HS data fusion.All the commercial satellites (SPOT, LANDSAT, IRS, IKONOS, Quickbird and Orbview) collect a high spatial resolution panchromatic image and multiple (usually four) multispectral images with significant lower spatial resolution. The PAN images are characterised by a very high spatial information content well-suited for intermediate scale mapping applications and urban analysis. The multispectral images provide the essential spectral information for smaller scale thematic mapping applications such as landuse surveys. Why don't most satellites collect high-resolution MS images directly, to meet this requirement for high-spatial and high-spectral resolutions? There is a limitation to the data volume that a satellite sensor can store on board and then transmit to ground receiving station. Usually the size of the panchromatic image is many times larger than the size of the multispectral images. The size of the panchromatic of Landsat ETM+ is four times greater than the size of a ETM+ multispectral image. The panchromatic image for IKONOS, Quickbird SPOT5 and Orbview is sixteen times larger than the respective multispectral images. As a result if a sensor collected high-resolution multispectral data it could acquire fewer images during every pass. Considering these limitations, it is clear that the most effective solution for providing high-spatial-resolution and high-spectral-resolution remote sensing images is to develop effective image fusion techniques. Image fusion is a technique used to integrate the geometric detail of a high-resolution panchromatic (Pan) image and the color information of a low-resolution multispectral (MS) image to produce a high-resolution MS image. During the last twenty years many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, IHS Transform have been developed producing good quality fused images. Despite the quite good optical results many research papers have reported the limitations of the above fusion techniques. The most significant problem is color distortion. Another common problem is that the fusion quality often depends upon the operator's fusion experience, and upon the data set being fused. No automatic solution has been achieved to consistently produce high quality fusion for different data sets. More recently new techniques have been proposed such as the Wavelet Transform, the Pansharp Transform and the Modified IHS Transform. Those techniques seem to reduce the color distortion problem and to keep the statistical parameters invariable. In this study we compare the efficiency of eight fusion techniques and more especially the efficiency of Multiplicative Brovey, IHS, Modified IHS, PCA, Pansharp, Wavelet and LMM (Local Mean Matching) fusion techniques for the fusion of Ikonos data. For each merged image we have examined the optical qualitative result and the statistical parameters of the histograms of the various frequency bands, especially the standard deviation All the fusion techniques improve the resolution and the optical result. The Pansharp, the Wavelet and the Modified IHS merging technique do not change at all the statistical parameters of the original images. These merging techniques are proposed if the researcher want to proceed to further processing using for example different vegetation indexes or to perform classification using the spectral signatures.
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In this paper, a new technique for improving the spatial resolution of hyperspectral image data will be presented. This technique combines a high-resolution image with a lower spatial resolution hyperspectral image to produce a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the panchromatic image. Hyperspectral imaging systems are assuming a greater importance for a wide variety of commercial and military systems. There have been several approaches to using a single higher spatial resolution band to improve the spatial resolution of the hyperspectral data. This algorithm offers a new approach to the problem of combining hyperspectral data with high-resolution images, and it is based and generally shows lower levels of error than the statistically based algorithms.
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In remote sensing multispectral and hyperspectral imaging are a valid method to analyse Earth Observation (EO) data.If multispectral imaging is largely used and well knowed in EO and refers to the scomposition of the spectral range of the instruments onboard in few channels (typically from 6 to 12), hyperspectral Imaging gains a greater spectral resolution and refers to obtain the spectrum for each pixel in the image, with the purpose of finding objects, identifying materials, or detecting processes through the structural analysis of the source (in SWIR range) or chemical behaviour (in VIS range).In this work a multispectral (NDVI) and hyperspectral (red edge slope) technique is used to perform a Change Detection (CD) on the vegetation of the Appia Antica Regional Park in Rome.The results show the benefits of these analyses in evaluating the state of landscape and in developing appropriate management projects.
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Hyperspectral satellite imaging has been gaining enormous research attention due to the latest advancements in the sensor technology, and the amount of information it conveys. However, its efficient analysis, transfer, and storage are still big practical issues which need to be endured in on-board applications. In this paper, we verify if the simulated multispectral data (with significantly smaller number of bands) can be segmented with accuracy as high as obtained over its corresponding original hyperspectral imagery. Our experimental study, backed up with statistical tests, revealed that it is possible to dramatically decrease the transfer and storage requirements of the original hyperspectral data by simulating its multispectral counterpart without adversely affecting the classification performance of popular supervised learners.
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Hyperspectral imager suite (HISUI) is the Japanese next-generation earth-observing sensor composed of hyperspectral and multispectral imagers. Unmixing-based fusion of hyperspectral and multispectral data enables the production of high-spatial-resolution hyperspectral data. HISUI simulated imaging system combining two imagers was developed for verification experiments to investigate the feasibility and clarify the whole procedure of the hyperspectral and multispectral data fusion mission on HISUI. Airborne experiments are planned as simulation tests of HISUI higher-order products. The experimental results of the ground based observation showed the importance of the preprocessing and cross-calibration on the final quality of fused data, which contributes to the practical use of hyperspectral and multispectral data fusion.
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Abstract. Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.
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Motivated by the increasing importance of hyperspectral remote sensing data and encouraged to improve the use of physical-mathematical models that a necessity multispectral image, this text wants to perform a abstract of correspondence between hyperspectral and multispectral information to improve of zonation environmental and plant species. In order, the use hyperspectral remote sensing has been promising in to characterize chemical and physical properties of vegetation. When combined with multispectral remote sensing may promote progress in monitoring the physical and chemical properties of spatial and temporal scale.
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