A novel band selection and spatial noise reduction method for hyperspectral image classification.

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI

Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification.

Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples

Study of catalytic hydrodeoxygenation performance for the Ni/KIT-6 catalysts

Ni/KIT-6 catalysts loaded with different amounts of metallic Ni were prepared by impregnation method. The prepared catalysts and their precursors were investigated through wide- and low-angle XRD, TEM, BET, H2-TPR, and H2-TPD analyzes. The catalytic hydrodeoxygenation performance of the catalysts was evaluated using ethyl acetate as a model bio oil compound. Results indicate that the catalytic hydrodeoxygenation performance of the prepared catalysts was directly related to hydrogen storage properties, hydrogen desorption properties, dispersion of the active component Ni, and so on. The ethyl acetate conversion and ethane selectivity of 25 wt% Ni/KIT-6 catalyst were 100 and 96.8%, respectively, at 300 °C, which shows the best performance. The hydrodeoxygenation activity of ethyl acetate was higher than that of methyl acetate and isopropyl acetate because of the effect of molecular polarity and size. And, this reaction is a structure sensitive reaction. Keywords: Mesoporous Ni-based catalyst, Active Ni species, Pore structure, Hydrodeoxygenation, Ethyl acetat

A species of magnetotactic deltaproteobacterium was detected at the highest abundance during an algal bloom

International audienceMagnetotactic bacteria (MTB) are a group of microorganisms that have the ability to synthesize intracellular magnetic crystals (magnetosomes). They prefer microaerobic or anaerobic aquatic sediments. Thus, there is growing interest in their ecological roles in various habitats. In this study we found co-occurrence of a large rod-shaped deltaproteobacterial magnetotactic bacterium (tentatively named LR-1) in the sediment of a Downloaded from https://academic.oup.com/femsle/advance-article-abstract/doi/10.1093/femsle/fnz253/5681391 by guest on 23 December 201

Diversity and Characterization of Multicellular Magnetotactic Prokaryotes From Coral Reef Habitats of the Paracel Islands, South China Sea

International audienceWhile multicellular magnetotactic prokaryotes (MMPs) are ubiquitous in marine environments, the diversity of MMPs in sediments of coral reef ecosystems has rarely been reported. In this study, we made an investigation on the diversity and characteristics of MMPs in sediments at 11 stations in coral reef habitats of the Paracel Islands. The results showed that MMPs were present at nine stations, with spherical mulberry-like MMPs (s-MMPs) found at all stations and ellipsoidal pineapple-like MMPs (e-MMPs) found at seven stations. The maximum abundance of MMPs was 6 ind./cm 3. Phylogenetic analysis revealed the presence of one e-MMP species and five s-MMP species including two species of a new genus. The results indicate that coral reef habitats of the Paracel Islands have a high diversity of MMPs that bio-mineralize multiple intracellular chains of iron crystals and play important role in iron cycling in such oligotrophic environment. These observations provide new perspective of the diversity of MMPs in general and expand knowledge of the occurrence of MMPs in coral reef habitats