Papers Hyperspectral Image Classification
“Hyperspectral Image Classification” 태그가 달린 논문 329편 · 필터 해제
Dimensionality Reduction for Hyperspectral Image Classification
This paper addresses the issue of supervised classification in the context of hyperspectral satellite images. It deals with two fundamental aspects: dimensionality reduction of data and the selection of appropriate super…
Hyperspectral Image ClassificationDimensionality ReductionA Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning
The small-sample-size (SSS) problem remains a fundamental challenge in machine learning when labeled data are scarce due to cost, accessibility, or ethical constraints. While numerous approaches have been proposed, exist…
Hyperspectral Image ClassificationMedical DiagnosisFace RecognitionFault DiagnosisConvolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks
Feature extraction for hyperspectral image classification is conventionally addressed using rigid tensor decompositions that fail to capture complex spatio-spectral interdependencies, or heavily parameterized convolution…
Hyperspectral Image ClassificationTopology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification
Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios. However, privacy rules or storage limits often block access to data f…
Hyperspectral Image ClassificationDomain AdaptationMSCM-net: A hyperspectral image classiffcation method based on multi-scale convolution and Mamba
Hyperspectral imaging is widely used in remote sensing and engineering. Therefore, research on its classification methods is crucial. While CNN and Transformer-based methods have advanced, they still face locality constr…
Hyperspectral Image ClassificationBCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
Hyperspectral image (HSI) classification systems are increasingly deployed on platforms with strict computational budgets, such as UAVs and small spaceborne sensors. In these settings, accuracy alone is not enough; the m…
Hyperspectral Image ClassificationRepresentation LearningDAPGNet: Dynamic Adaptive Physics-Guided Graph Diffusion Network for Hyperspectral Image Classification
Hyperspectral image (HSI) classification requires reliable pixel-relation modeling under spectral variability, mixed pixels, and heterogeneous boundaries. Existing graph-based HSI classifiers usually construct graph topo…
Hyperspectral Image ClassificationGraph LearningMBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models
Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream hyperspectral image (HSI) classification …
Hyperspectral Image ClassificationData Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classification. The proposed model, termed DE-CFFN, retains the original two-st…
Hyperspectral Image ClassificationDimensionality ReductionMixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
In this paper, a novel framework, MixerSENet, is introduced for hyperspectral image (HSI) classification, designed to address the challenges of computational efficiency and limited labeled data. The proposed model proces…
Hyperspectral Image ClassificationComputational EfficiencySoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification
Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning various fields. Owing to the inclusion of unknown categories in the targ…
Hyperspectral Image ClassificationDomain AdaptationHyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
Hyperspectral image classification demands spatially coherent predictions and precise boundary delineation. Yet prevailing superpixel-based methods face an inherent contradiction: clustering aggregates similar pixels int…
Hyperspectral Image ClassificationHigh-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification
Recently, Hyperspectral Image (HSI) classification has attracted increasing attention in remote sensing. However, HSI data are inherently high-dimensional but low-rank, with discriminative information concentrated on a l…
Hyperspectral Image ClassificationMixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestrial objects by capturing detailed, continuous spectral information. T…
Hyperspectral Image ClassificationSemantic SegmentationA Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification
In the hyperspectral image (HSI) classification task, each pixel is categorized into a specific land-cover category or material. Convolutional neural networks (CNNs) and transformers have been widely used to extract loca…
Hyperspectral Image ClassificationConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification
Hyperspectral image (HSI) classification remains challenging due to high spectral dimensionality, redundancy, and limited labeled data. Although convolutional neural networks (CNNs) and Vision Transformers (ViTs) achieve…
Hyperspectral Image ClassificationUnmixing-Guided Spatial-Spectral Mamba with Clustering Tokens for Hyperspectral Image Classification
Although hyperspectral image (HSI) classification is critical for supporting various environmental applications, it is a challenging task due to the spectral-mixture effect, the spatial-spectral heterogeneity and the dif…
Hyperspectral Image ClassificationCosine-Normalized Attention for Hyperspectral Image Classification
Transformer-based methods have improved hyperspectral image classification (HSIC) by modeling long-range spatial-spectral dependencies; however, their attention mechanisms typically rely on dot-product similarity, which …
Hyperspectral Image ClassificationRepresentation LearningLGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification
Deep learning methods, including Convolutional Neural Networks, Transformers and Mamba, have achieved remarkable success in hyperspectral image (HSI) classification. Nevertheless, existing methods exhibit inflexible inte…
Hyperspectral Image Classification3D Fourier-based Global Feature Extraction for Hyperspectral Image Classification
Hyperspectral image classification (HSIC) has been significantly advanced by deep learning methods that exploit rich spatial-spectral correlations. However, existing approaches still face fundamental limitations: transfo…
Hyperspectral Image ClassificationRepresentation Learning