Zheng Li

Deep learning-based hyperspectral image (HSI) classification has significant applications in remote sensing scene understanding. Whole-image propagation classification methods can achieve high-precision classification at the full-pixel level with a significant efficiency advantage. However, such methods generally face challenges such as the smoothness of high-frequency details and difficulty in efficiently modeling deep features. To address these issues, this article proposes a fast multikernel graph convolutional network (FMGCN) for fast whole-image classification. The model designs a multikernel graph convolution (MKGC) algorithm to enhance the expression ability of deep spectral–spatial features. This algorithm generalizes the graph convolution process from a single kernel propagation process to a propagation process with an arbitrary number of kernels to more fully exploit spectral–spatial features in non-Euclidean spaces. In addition, a symmetric compressed refinement network (SCRN) is constructed to restore the weakened local structural information in the whole-image propagation, and an embedded edge compensation strategy (EECS) is combined to further strengthen the high-frequency features in the boundary regions. Based on this design, FMGCN is able to capture more comprehensive global dependency information in a single whole-image propagation, achieving a uniform balance of high accuracy and high efficiency. Experiments on four representative hyperspectral datasets show that FMGCN achieves the best classification performance, effectively alleviates over-smoothing in complex structural scenarios, and outperforms batch propagation models in terms of inference efficiency.

Small weak object detection (SWOD) is a significant but neglected task in remote sensing image interpretation. Due to limitations in imaging resolution and inherent characteristics of the objects, detection networks struggle to effectively extract semantic features, which are crucial for object identification and recognition. In recent years, graph convolutional networks (GCNs) have been developed to handle non-Euclidean data. Through GCNs, node data are enriched via aggregation and propagation across the graph. In this article, we explore the feasibility of GCNs in semantic clue extraction to address the lack of key semantics in small weak objects. First, we propose a multi-head graph reasoning learning model (MGRL) that projects initial feature representations into graph space and utilizes a two-layer multi-head graph network to extract essential semantic information. Second, we introduce a foreground-background binary masking technique (FBM) that roughly segments the foreground region of the image. The mask is converted into a prior prompt, which is then incorporated into the adjacency matrix, emphasizing object reasoning in MGRL. Next, we present a cross learning-based feature alignment learning module (FAL) to resolve feature misalignment issues caused by spatial projection. Finally, we adopt a cross-layer semantic interaction module (CSI) to facilitate cross-layer communication and aggregation of features. Extensive experiments are conducted on five remote sensing datasets: DIOR, AI-TOD, NWPU VHR-10, DOTA-v1.0, and STAR. The experimental results demonstrate the superior performance and advantages of our method.

Arbitrary-oriented ship detection is a challenging task in optical remote sensing images (ORSIs). Existing methods mainly focus on improving model structures but seldom consider ship characteristics, such as shape and multi-scale distribution. This results in prior ship-related information not being effectively utilized. To address these issues, a ship detector based on ellipse feature encoding and balanced decoupling label assignment strategy (EBDet) is proposed, aiming to effectively integrate ship features during the crucial detection process. Specifically, the Ellipse Feature Encoding (EFE) strategy incorporates ship shape to represent the oriented bounding boxes (OBB) with seven parameters, and addresses the boundary discontinuity problem by resolving the contradiction between angular discreteness and network continuity. The Ellipse Balanced Sampling (EBS) strategy fits the size and shape of ships to achieve a balance in the number of positive samples between ships of different scales. The Dynamic Decoupling Label Assignment (DDLA) strategy adaptively evaluates the sample space sensitivity, and mitigates the feature misalignment problem by decoupling positive samples in classification and regression. Extensive experiments demonstrate the effectiveness and advancement of our EBDet. Specifically, on the HRSC2016 dataset, EBDet achieves mAP of 90.53% and 90.64% with ResNet-50 and ResNet-101 backbones, respectively. On the DIOR-Ship dataset, EBDet achieves mAP of 86.69%.

Benefiting from the development of deep learning, the super-resolution technology for remote sensing hyperspectral images (HSIs) has achieved impressive progress. However, due to the high coupling of complex components in remote sensing HSIs, it is challenging to achieve a complete characterization of the internal information, which in turn limits the precise reconstruction of detailed texture and spectral features. Therefore, we propose the multi-attitude hybrid network (MAHN) for extracting and characterizing information from multiple feature spaces. On the one hand, we construct the spectral hypergraph cross-attention module (SHCAM) and the spatial hypergraph self-attention module (SHSAM) based on the high and low-frequency features in the spectral and the spatial domains, respectively, which are used to capture the main structure and detail changes within the image. On the other hand, high-level semantic information in mixed pixels is parsed by spectral mixture analysis, and semantic hypergraph 3D module (SH3M) are constructed based on the abundance of each category to enhance the propagation and reconstruction of semantic information. Furthermore, to mitigate the domain discrepancies among features, we introduce a sensitive bands attention mechanism (SBAM) to enhance the cross-guidance and fusion of multi-domain features. Extensive experiments demonstrate that our method achieves optimal reconstruction results compared to other state-of-the-art algorithms while effectively reducing the computational complexity.

Existing mainstream hyperspectral image (HSI) classification frameworks, like multilayer perceptron (MLP) networks, combine learnable linear transformations with nonlearnable nonlinear activation functions. While these feature extraction algorithms achieve high classification accuracy, they require significant computational and memory resources, and many models lack adequate informative representations. Recently, fast Kolmogorov-Arnold networks (FKANs) have emerged as a lightweight alternative to MLPs, offering competitive performance with fewer parameters and lower computational costs. This paper presents a hierarchical progressive fusion network (HPFN) based on FKAN to address these challenges. The proposed network explores HSI information more comprehensively through branches at three scale levels (pixel, patch, global) while remaining lightweight. The orthogonality of information at different levels is preserved to some extent for diverse practical applications. We develop two FKAN based lightweight convolutional networks to extract pixel-level and patch-level features. In the global-level branch, a boundary-enhanced U-Net with an edge-constrained module based on Sobel operators mitigates over-smoothing. The cross-scale guided fusion mechanism allows for dynamic awareness and accurate pairing of pixel-level features with local and global features, reducing information redundancy. Experimental results show that the proposed network achieves competitive classification accuracy on four public datasets at low computational cost, validating its effectiveness.

The joint semantic segmentation of multi-modal remote sensing images can make up for the problem of insufficient features of single-modal images and effectively improve the classification accuracy. Some deep learning methods have achieved good performance, but they face problems such as complex network structure, large number of parameters, and difficulty in deployment. In this paper, more attention is paid to front-end and branch-level feature transformation to obtain multi-scale semantic information. The multi-scale dilated extraction module (MDEM) is constructed to mine the specific features of different modalities. The multi-modal complementary attention module (MCAM) is designed for further acquiring prominent complementary content. The concatenated features are transmitted and reused by the dense convolution to complete the encoding. Ultimately, a general and concise end-to-end model is proposed. Comparative experiments are carried out on three heterogeneous datasets, and the model put forward performs well in qualitative analysis, quantitative comparison and visual effect. Meanwhile, the dexterity and practicability of the model are more prominent, which can provide support for lightweight design and hardware deployment.

Despite advances in remote sensing object detection, accurately identifying small, weak objects remains challenging. Their limited pixel representation often fails to capture distinctive features, making them susceptible to environmental interference. Current detectors frequently miss these subtle feature variations. To address these challenges, we propose FCDet, a feature contrast-based detector for small, weak objects. Our approach introduces: (1) a spatial-guided feature upsampler (SGFU) that aligns features by adaptive sampling based on spatial distribution, thus achieving fine-grained alignment during feature aggregation; (2) a feature contrast head (FCH) that projects GT and RoI features into an embedding space for discriminative learning; and (3) an instance-controlled label assignment (ICLA) strategy that optimizes sample selection for feature contrastive learning. We conduct comprehensive experiments on challenging datasets, with the proposed method achieving 73.89% mAP on DIOR, 95.04% mAP on NWPU VHR-10, and 26.4% AP on AI-TOD, demonstrating its effectiveness and superior performance.

Object detection plays a crucial role in remote sensing due to the urgent demands of various applications, such as urban planning and environmental monitoring. Despite notable progress, current methods still struggle with detecting challenging small objects. At the object level, the limited pixel representation, blurred details, and background interference of small objects impose greater demands on feature extractors. At the network level, resource bias fails to provide adequate learning signals for these objects. In this paper, we propose a Sparse Transformer-based detector (STDet) to tackle these challenges. Specifically, we design a Local-to-Global Transformer network (LGFormer) to explore essential feature representations. The Local Transformer Block establishes correlations between tokens and their surrounding data, while the Global Transformer Block captures long-distance dependencies related to the objects. Meanwhile, we introduce a Scale-Balanced Label Assignment (SBLA) strategy that considers more samples to small objects. SBLA dynamically shifts the learning focus to easily overlooked objects and alleviates the issue of sample imbalance. Extensive experiments on three large-scale remote sensing datasets demonstrate the effectiveness of STDet and its superiority in small object detection.