To mine the spectral-spatial information of target pixel in hyperspectral image classification (HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input pattern, where a patch represents its central pixel and the neighbor pixels play auxiliary roles in the classification process. However, compared to the central pixel, its neighbor pixels often have different contributions for classification. Although many existing patch-based CNNs could adaptively emphasize the spatial neighbor information, most of them ignore the latent relationship between the center pixel and its neighbor pixels. Moreover, efficient spectral-spatial feature extraction has been a difficult yet vital topic for HSIC. To address the mentioned problems, a central vector oriented self-similarity network (CVSSN) is proposed for HSIC. Specifically, based on two similarity measures, we firstly design an adaptive weight addition based spectral vector self-similarity module (AWA-SVSS) in input space and a Euclidean distance based feature vector self-similarity module (ED-FVSS) in feature space to fully mine the central vector oriented spatial relationships. Besides, a spectral-spatial information fusion module (SSIF) is formulated as a new pattern to fuse the central 1D spectral vector and the corresponding 3D patch for efficient spectral-spatial feature learning of the subsequent modules. Moreover, we implement a channel spatial separation convolution module (CSS-Conv) and a scale information complementary convolution module (SIC-Conv) for efficient spectral-spatial feature learning. Extensive experimental results on four popular HSI data sets demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art methods.

Semantic segmentation is an extremely challenging task in high-resolution remote sensing (HRRS) images as objects have complex spatial layouts and enormous variations in appearance. Convolutional neural networks (CNNs) have excellent ability to extract local features and have been widely applied as the feature extractor for various vision tasks. However, due to the inherent inductive bias of convolution operation, CNNs inevitably have limitations in modeling long-range dependencies. Transformer can capture global representations well, but unfortunately ignores the details of local features and has high computational and spatial complexity in processing high-resolution feature maps. In this paper, we propose a novel hybrid architecture for HRRS image segmentation, termed EMRT, to exploit the advantages of convolution operations and Transformer to enhance multi-scale representation learning. We incorporate the deformable self-attention mechanism in the Transformer to automatically adjust the receptive field, and design an encoder-decoder architecture accordingly to achieve efficient context modeling. Specifically, the CNN is constructed to extract feature representations. In the encoder, local features and global representations at different resolutions are extracted by the CNN and Transformer, respectively, and fused in an interactive manner. Moreover, a separate spatial branch is designed to extract multi-scale contextual information as queries, and global dependencies between features at different scales are efficiently established by the decoder. Extensive experiments on three public remote sensing datasets demonstrate the superiority of EMRT and indicate that the overall performance of our method outperforms state-of-the-art methods. Code is available at https://github.com/peach-xiao/EMRT.

For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral-spatial relationships has attracted widespread attention in hyperspectral image classification (HSIC) community. However, there are still some intractable obstructs. For one thing, in the patch based processing pattern, some spatial neighbor pixels are often inconsistent with the central pixel in land-cover class. For another thing, linear and nonlinear correlations between different spectral bands are vital yet tough for representing and excavating. To overcome these mentioned issues, an adaptive mask sampling and manifold to Euclidean subspace learning (AMS-M2ESL) framework is proposed for HSIC. Specifically, an adaptive mask based intra-patch sampling (AMIPS) module is firstly formulated for intra-patch sampling in an adaptive mask manner based on central spectral vector oriented spatial relationships. Then, based on distance covariance descriptor, a dual channel distance covariance representation (DC-DCR) module is proposed for modeling unified spectral-spatial feature representations and exploring spectral-spatial relationships, especially linear and nonlinear interdependence in spectral domain. Furthermore, considering that distance covariance matrix lies on the symmetric positive definite (SPD) manifold, we implement a manifold to Euclidean subspace learning (M2ESL) module respecting Riemannian geometry of SPD manifold for high-level spectral-spatial feature learning. Additionally, we introduce an approximate matrix square-root (ASQRT) layer for efficient Euclidean subspace projection. Extensive experimental results on three popular HSI data sets with limited training samples demonstrate the superior performance of the proposed method compared with other state-of-the-art methods. The source code is available at https://github.com/lms-07/AMS-M2ESL.

Nowadays, the method of simple-feature extraction has been extensively studied and is used in PPG biometric recognition; some promising results have been reported. However, some useful information is often lost in the process of PPG signal denoising; the time-domain, frequency-domain, or wavelet feature extracted is often partial, which cannot fully express the raw PPG signal; and it is also difficult to choose the appropriate matching method. Therefore, to make up for these shortcomings, a method of PPG biometric recognition based on multifeature extraction and naive Bayes classifier is proposed. First, in the preprocessing of the raw PPG data, the sliding window method is used to rerepresent the raw data. Second, the feature-extraction methods based on time-domain, frequency-domain, and wavelet are analysed in detail, then these methods are used to extract the time-domain, frequency-domain, and wavelet features, and the features are concatenated into a multifeature. Finally, the multifeature is normalized and combined with classifiers and Euclidean distance for matching and decision-making. Extensive experiments are conducted on three PPG datasets, it is found that the proposed method can achieve a recognition rate of 98.65%, 97.76%, and 99.69% on the respective sets, and the results demonstrate that the proposed method is not inferior to several state-of-the-art methods.

Change detection (CD) aims to identify surface changes from bi-temporal remote sensing (RS) images, which is a crucial and challenging topic in RS. In recent years, RS images CD has achieved significant advancements through the use of convolutional neural networks (CNNs). However, existing deep learning-based CD methods still face some challenges such as blurry boundaries, pseudo changes caused by fluctuations in imaging conditions, and complexity of change objects in the scene. In this study, we present a novel perspective for exploring the cross-temporal interaction in the feature fusion stage and propose a bi-temporal feature exchange and enhancement network (ExNet). Specifically, we argue that the heterogeneity of bi-temporal RS images leads to the failure of the change detection model in some cases. Therefore, we attempt to achieve feature alignment in two aspects by feature exchange. On one hand, we exchange the statistical information of bi-temporal features, facilitating the transfer of their style. On the other hand, bi-temporal features are composed of content correlation embeddings (CCEs) and domain correlation embeddings (DCEs). We design a dynamic low-pass filter (DLF) to partially extract and exchange DCEs in bi-temporal features to achieve the feature distribution alignment. Moreover, a frequency separation enhancement module (FSEM) is proposed, which transforms the fused features into the frequency domain and enhances the corresponding frequency representation. Comprehensive experimental results on three popular CD datasets demonstrate the effectiveness and efficiency of ExNet compared with state-of-the-art methods. The source code will be accessible at https://github.com/liuyk97/ExNet.