An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms

Sharpening the VNIR and SWIR Bands of Sentinel-2A Imagery through Modified Selected and Synthesized Band Schemes

In this work, the bands of a Sentinel-2A image with spatial resolutions of 20 m and 60 m are sharpened to a spatial resolution of 10 m to obtain visible and near-infrared (VNIR) and shortwave infrared (SWIR) spectral bands with a spatial resolution of 10 m. In particular, we propose a two-step sharpening algorithm for Sentinel-2A imagery based on modified, selected, and synthesized band schemes using layer-stacked bands to sharpen Sentinel-2A images. The modified selected and synthesized band schemes proposed in this study extend the existing band schemes for sharpening Sentinel-2A images with spatial resolutions of 20 m and 60 m to improve the pan-sharpening accuracy by changing the combinations of bands used for multiple linear regression analysis through band-layer stacking. The proposed algorithms are applied to the pan-sharpening algorithm based on component substitution (CS) and a multiresolution analysis (MRA), and our results are then compared to the sharpening results when using sharpening algorithms based on existing band schemes. The experimental results show that the sharpening results from the proposed algorithm are improved in terms of the spatial and spectral properties when compared to existing methods. However, the results of the sharpening algorithm when applied to our modified band schemes show differing tendencies. With the modified, selected band scheme, the sharpening result when applying the CS-based algorithm is higher than the result when applying the MRA-based algorithm. However, the quality of the sharpening results when using the MRA-based algorithm with the modified synthesized band scheme is higher than that when using the CS-based algorithm

Automatic Extraction of Optimal Endmembers from Airborne Hyperspectral Imagery Using Iterative Error Analysis (IEA) and Spectral Discrimination Measurements

Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials

A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs

The objective of this work is to develop an algorithm for pansharpening of very high resolution (VHR) satellite imagery that reduces the spectral distortion of the pansharpened images and enhances their spatial clarity with minimal computational costs. In order to minimize the spectral distortion and computational costs, the global injection gain is transformed to the local injection gains using the normalized difference vegetation index (NDVI), on the assumption that the NDVI are positively or negatively correlated with local injection gains obtained from each band of the satellite data. In addition, the local injection gains are then applied in the hybrid pansharpening algorithm to optimize the spatial clarity. In particular, in the proposed algorithm, a synthetic intensity image is determined using block-based linear regression. In experiments using imagery collected by various satellites, such as KOrea Multi-Purpose SATellite-3 (KOMPSAT-3), KOMPSAT-3A and WorldView-3, the pansharpened results obtained using the proposed Hybrid Pansharpening algorithm using NDVI and based on the spectral mode (HP-NDVIspectral) provide a better representation of the values of the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), the spectral angle mapper (SAM) and the Q4/Q8 than those produced by existing pansharpening algorithms. In terms of spatial quality, the pansharpened images obtained using the proposed pansharpening algorithm based on the spatial mode (HP-NDVIspatial) have higher average gradient (AG) values than those obtained using existing pansharpening methods. In addition, the computational complexity of our method is similar to that of a pansharpening algorithm that is based on a global injection model, although our methodology has characteristics that are similar to those of a local injection gain-based model that has a very high computational cost. Thus, the quantitative and qualitative assessments presented here indicate that the proposed algorithm can be utilized in various applications that employ spectral information or require high spatial clarity

Room???Temperature Crosslinkable Natural Polymer Binder for High???Rate and Stable Silicon Anodes

Natural polymers with abundant side functionalities are emerging as a promising binder for high???capacity yet large???volume???change silicon anodes with a strong and reversible supramolecular interaction that originates from secondary bonding. However, the supramolecular network solely based on hydrogen bonding is relatively vulnerable to repeated deformation and has an insufficient diffusivity of lithium ions. Herein, reported is a facile but efficient way of incorporating the natural polymers with an ionically conductive crosslinker, which can construct a robust network for silicon anodes. The boronic acid in the crosslinker spontaneously reacts with natural polymers to generate boronic esters at room temperature without any kind of triggers, which gives a strong and dynamic covalent bonding to the supramolecular network. The other component in the crosslinker, polyethylene oxide, contributes to the enhanced ionic conductivity of polymers, leading to outstanding rate performances even at a high mass loading of silicon nanoparticles (>2 mg cm???2). The small portion of the proposed crosslinker can modulate the strength of the entire network by balancing the covalent crosslinking and self???healing secondary interaction along with the fast lithium???ion diffusion, thus enabling the extended operation of silicon electrodes

Metamorphosis of Seaweeds into Multitalented Materials for Energy Storage Applications

Transition metal ion dissolution due to hydrofluoric acid attack is a long???standing issue in the Mn???based spinel cathode materials of lithium???ion batteries (LIBs). Numerous strategies have been proposed to address this issue, but only a fragmentary solution has been established. In this study, reported is a seaweed???extracted multitalented material, namely, agar, for high???performance LIBs comprising Mn???based cathode materials at a practical loading density (23.1 mg cm???2 for LiMn2O4 and 10.9 mg cm???2 for LiNi0.5Mn1.5O4, respectively). As a surface modifier, 3???glycidoxypropyl trimethoxysilane (GPTMS) is employed to enable the agar to have different phase separation behaviors during the nonsolvent???induced phase separation process, thus eventually leading to the fabrication of an outstanding separator membrane that features a well???defined porous structure, superior mechanical robustness, high ionic conductivity, and good thermal stability. The GPTMS???modified agar separator membrane coupled with a pure agar binder to the LiNi0.5Mn1.5O4/graphite full cell leads to exceptional improvement in electrochemical performance outperforming binders and separator membrane in current commercial products even at 55 ??C; this improvement is due to beneficial features such as Mn2+ chelation and PF5 stabilizing capabilities. This study is believed to provide insights into the potential energy applications of natural seaweeds

Electrochemical scissoring of disordered silicon-carbon composites for high-performance lithium storage

Practically adapted physical integration of silicon and carbon predominates as a viable solution to realize high energy density batteries, however, the composite structure is vulnerable to fracture. Here we report a molecular-level mixed silicon-carbon composite anode through thermal pyrolysis of silane and subsequent mechanical mill, entailed by electrochemical dissociation and reclustering of such disordered silicon-carbon bonds during the cycles. Lithium insertion induces heterolytic fission of the bonds into sub-nanometre silicon particles segregated by redox-active carbon framework validated by microscopy analysis and reactive molecular dynamics simulation. The embedded structure with a high packing density of silicon prevents detrimental electrochemical coalescence and direct contact to a liquid electrolyte to stabilize the interfaces, while three-dimensional (3D) carbon framework buffers large volume expansion of silicon to enable an extended full battery cycling