The massless Dirac electron transport in graphene has led to a variety of unique light-matter interaction phenomena, which promise many novel optoelectronic applications. Most of the effects are only accessible by breaking the spatial symmetry, through introducing edges, p-n junctions, or heterogeneous interfaces. The recent development of direct synthesis of lateral heterostructures offers new opportunities to achieve the desired asymmetry. As a proof of concept, we study the photothermoelectric effect in an asymmetric lateral heterojunction between the Dirac semimetallic monolayer graphene and the parabolic semiconducting monolayer MoS₂. Very different hot-carrier cooling mechanisms on the graphene and the MoS₂ sides allow us to resolve the asymmetric thermalization pathways of photoinduced hot carriers spatially with electrostatic gate tunability. We also demonstrate the potential of graphene-2D semiconductor lateral heterojunctions as broadband infrared photodetectors. The proposed structure shows an extreme in-plane asymmetry and provides a new platform to study light-matter interactions in low-dimensional systems.

Most hyperspectral image (HSI) classification methods rely on square patch sampling to incorporate spatial information, thereby facilitating the label prediction of the center pixel. However, square patch sampling introduces numerous heterogeneous pixels, which could distort the label prediction of center pixel. Moreover, it generates fixed training patch sample for each center pixel, hampering the performance of transformer-based models requiring a large number of training data. To address the above problems, we proposed Center Attention Transformer (CAT) with stratified spatial-spectral token generated by superpixel sampling for HSI classification. Firstly, to mitigate the inference of heterogeneous pixels, we propose Sampling From Superpixel Region mechanism to generate purer image cubes than traditional square neighborhood. Secondly, to expand the training data for transformer, we propose Multiple Stratified Random Sampling mechanism, which generates ample training samples without introducing additional labels. Finally, to more effectively extract information from the sampled patch tokens, we propose Spatial Spectral Token Generation mechanism and Center Attention Transformer structure with Gaussian Positional Embedding. This framework can extract long-range correlations of spectral information and pay more attention on the center pixel in spatial dimension. Experimental results on three HSI datasets demonstrate the performance of our proposed method CAT outperforms several state-of-the-art methods. The code of this work is available at https://github.com/fengjiaqi927/CAT-Center_Attention_Transformer.

Band selection has become an effective method to reduce hyperspectral dimensionality. In this letter, an information-assisted density peak index (IaDPI) is proposed to prioritize the bands. Based on a clustering method by finding density peaks, IaDPI introduces the intraband information entropy into the local density and intercluster distance to ensure cluster centers with a high quality. Also, the band distance is integrated with channel proximity to control the compactness of local density. Owing to the intraband entropy and the interband weighted dissimilarity, the selected band set with top-ranked IaDPI scores can hold high local density, clear global distinction, and good informative quality. Experimental results on real hyperspectral data indicate the advantages of the proposed IaDPI in good selection quality, robust noise immunity, and high classification accuracy.

Hyperspectral images (HSIs) contain rich context information in the spatial domain and spectral domain. To fully explore that information, a data-driven joint spatial–spectral attention network (JSSAN) is proposed in this letter. Specifically, we first design a spatial–spectral attention ( $\text{S}^{2}\text{A}$ ) block to simultaneously capture long-range interdependency of spatial and spectral data via the similarity evaluation. Then we adopt a weighted sum operation of features at all spatial positions and channels to selectively aggregate discriminative spatial–spectral features. Second, the $\text{S}^{2}\text{A}$ block is inserted into simple convolutional neural network (CNN) structure to extract more representative features for classification, by adaptively emphasizing features of informative land covers and spectral bands which contribute more to class identification. The experimental results reveal that our proposed method outperforms several state-of-the-art algorithms.

Semantic segmentation of high-resolution aerial images is a challenging task on account of complex scene-variation and large scale-difference. However, these two issues are inadequately addressed in general semantic segmentation methods. In this paper, we propose a Multi-scale Prototype Contrast Network (MPCNet) to improve the adaptive capability for different scenes and scales. Specifically, a novel multi-scale prototype transformer decoder (MPTD) is designed to extract dynamic scene-specific prototypes as pixel classifier by fusing information of feature maps and learnable class tokens. To exploit cross-scene context information and accommodate the large scale-difference in aerial image, we build a multi-scale prototype memory queue to store these multi-scale prototypes during training. Upon the multi-scale prototype memory queue, a novel multi-scale prototype contrastive loss is proposed to increase object feature discriminability across multiple scale, which brings better consistency of intermediate feature and boosts the convergence of network. Extensive experimental results on three publicly available datasets demonstrate the effectiveness and efficiency of our MPCNet over other state-of-the-art methods. The code is available at https://github.com/qixiong-wang/mmsegmentation-mpcnet.