Preservation of spectral information and enhancement of spatial resolution are regarded as important issues in remote sensing satellite image fusion. In previous research, various algorithms have ...been proposed. Although they have been successful, there are still some margins of spatial and spectral quality that can be improved. In addition, a new method that can be used for various types of sensors is required. In this paper, a new adaptive fusion method based on component substitution is proposed to merge a high-spatial-resolution panchromatic (PAN) image with a multispectral image. This method generates high-/low-resolution synthetic component images by partial replacement and uses statistical ratio-based high-frequency injection. Various remote sensing satellite images, such as IKONOS-2, QuickBird, LANDSAT ETM+, and SPOT-5, were employed in the evaluation. Experiments showed that this approach can resolve spectral distortion problems and successfully conserve the spatial information of a PAN image. Thus, the fused image obtained from the proposed method gave higher fusion quality than the images from some other methods. In addition, the proposed method worked efficiently with the different sensors considered in the evaluation.
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth ...data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.
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Sodium (Na) ion batteries are interesting candidates for replacing lithium (Li) ion batteries, primarily due to the abundance of Na in the environment. However, the performance and energy density of ...a Na-ion battery are inferior to those of a Li-ion battery. Organic active materials can help overcome the drawbacks associated with Na-ion batteries because of their many advantages. However, such organic polymer electrodes are subjected to a high self-discharge and low practical capacity because the polymer electrode easily dissolves in an organic electrolyte and forms an insulating layer. Therefore, in this study, we have designed a unique organic electrode in which an active polymer is encapsulated into a carbon nanotube (CNT) to form an electrode with high polymer content. The CNT is able to retain the active polymer within the electrode structure, providing an effective electronic conduction path. Moreover, the CNT can contain large amounts of active polymer and therefore exhibits superior electrochemical properties without self-discharge, making it well suited for use as a cathode material in a Na-ion battery.
The Segment Anything Model (SAM) has had a profound impact on deep learning applications in remote sensing. SAM, which serves as a prompt-based foundation model for segmentation, exhibits a ...remarkable capability to “segment anything,” including building objects on satellite or airborne images. To facilitate building segmentation without inducing supplementary prompts or labels, we applied a sequential approach of generating pseudo-labels and incorporating an edge-driven model. We first segmented the entire scene by SAM and masked out unwanted objects to generate pseudo-labels. Subsequently, we employed an edge-driven model designed to enhance the pseudo-label by using edge information to reconstruct the imperfect building features. Our model simultaneously utilizes spectral features from SAM-oriented building pseudo-labels and edge features from resultant images from the Canny edge detector and, thus, when combined with conditional random fields (CRFs), shows capability to extract and learn building features from imperfect pseudo-labels. By integrating the SAM-based pseudo-label with our edge-driven model, we establish an unsupervised framework for building segmentation that operates without explicit labels. Our model excels in extracting buildings compared with other state-of-the-art unsupervised segmentation models and even outperforms supervised models when trained in a fully supervised manner. This achievement demonstrates the potential of our model to address the lack of datasets in various remote sensing domains for building segmentation.
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Harvesting energy from natural resources is of significant interest because of their abundance and sustainability. Seawater is the most abundant natural resource on earth, covering two‐thirds of the ...surface. The rechargeable seawater battery is a new energy storage platform that enables interconversion of electrical energy and chemical energy by tapping into seawater as an infinite medium. Here, an overview of the research and development activities of seawater batteries toward practical applications is presented. Seawater batteries consist of anode and cathode compartments that are separated by a Na‐ion conducting membrane, which allows only Na+ ion transport between the two electrodes. The roles and drawbacks of the three key components, as well as the development concept and operation principles of the batteries on the basis of previous reports are covered. Moreover, the prototype manufacturing lines for mass production and automation, and potential applications, particularly in marine environments are introduced. Highlighting the importance of engineering the cell components, as well as optimizing the system level for a particular application and thereby successful market entry, the key issues to be resolved are discussed, so that the seawater battery can emerge as a promising alternative to existing rechargeable batteries.
Rechargeable seawater batteries tap into earth‐abundant natural seawater as the active material to transform between electrical energy and chemical energy. The progress, challenges, and prospects of seawater batteries for practical applications are summarized.
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Sn4P3 is introduced for the first time as an anode material for Na‐ion batteries. Sn4P3 delivers a high reversible capacity of 718 mA h g−1, and shows very stable cycle performance with negligible ...capacity fading over 100 cycles, which is attributed to the confinement effect of Sn nanocrystallites in the amorphous phosphorus matrix during cycling.
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•Rechargeable seawater batteries are assembled using two Na+ conducting ceramics.•The battery with β″-Al2O3 electrolyte is irreversible even at 1 cycle.•The battery with NASICON shows ...a coulombic efficiency of 91% after 20 cycles.•NASICON is much more stable in seawater than β″-Al2O3.
This study describes the assembly of a rechargeable seawater battery using hard carbon as the anode, seawater as the cathode, and a fast Na ion-conducting ceramic as the solid electrolyte. Two different Na ion-conducting ceramics, β″-Al2O3 and Na3Zr2Si2PO12 (NASICON), are used as the solid electrolytes in this study. The discharge capacity of the seawater battery with the NASICON solid electrolyte is 120mAhg−1 after the first cycle and over 91% coulombic efficiency after twenty cycles. However, under the same experimental conditions, the discharge capacity of the seawater battery with a β"-Al2O3 electrolyte significantly drops to 10mAhg−1 after one cycle. It is observed that the stability of NASICON in seawater is superior to that of β"-Al2O3 and impedance results of NASICON are not changed significantly compared to that of β"-Al2O3 after cycling tests. The stability of Na ion-conducting ceramics in seawater and their effects on the electrochemical performance of seawater batteries are presented and discussed.
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Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under ...the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.
The fish-eye lens camera offers the advantage of efficient acquisition of image data through a wide field of view. However, unlike the popular perspective projection camera, a strong distortion ...effect appears as the periphery of the image is compressed. Such characteristics must be precisely analyzed through camera self-calibration. In this study, we carried out a fish-eye lens camera self-calibration while considering different types of test objects and projection models. Self-calibration was performed using the V-, A-, Plane-, and Room-type test objects. In the fish-eye lens camera, the V-type test object was the most advantageous for ensuring the accuracy of the principal point coordinates and focal length, because the correlations between parameters were relatively low. On the other hand, the other test objects were advantageous for ensuring the accuracy of distortion parameters because of the well-distributed image points. Based on the above analysis, we proposed, an accurate fish-eye lens camera self-calibration method that applies the V-type test object. The RMS-residuals of the proposed method were less than 1 pixel.
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Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning ...approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with unsupervised CD methods and a CD network subjected to postprocessing by effectively utilizing the deep learning technique and object information.
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