Sep 2026· International Journal of Multimedia Information Retrieval· Vol 15· 0 citations· 58 references
TL;DR
A probabilistic reformulation of FS-RSISC that moves beyond rigid point-based prototypes by modeling each class as a probability distribution over the hyperspherical feature space and adopts the von Mises–Fisher (vMF) distribution to capture both semantic uncertainty and feature diversity.
Existing class-incremental learning (CIL) methods for remote sensing (RS) scene classification often tend to be training-intensive or rely on static visual features that may inadequately capture the complex interclass similarity and intraclass diversity inherent in RS imagery. Moreover, directly reusing features from m...
Wen-Liang Du, Ji-Cun He, Jia-Qi Zhao et al.· IEEE Transactions on Geoscie...· 0 citations
DLPANet is proposed, a novel dual-level prototype alignment network centered on Prototype-Guided Spatial Attention, enabling simultaneous modeling of scene context and fine-grained details and demonstrates that the decoupled dual cross-attention mechanism provides superior prototype-query alignment compared to prior gl...
Mustafa Alawadi, M. Fateh· Jordanian Journal of Compute...· 0 citations
Few-shot object detection (FSOD) in remote sensing imagery faces critical challenges stemming from extreme data scarcity, specifically inadequate feature coverage, severe class imbalance, and pervasive incomplete annotations. To address these interconnected issues, this article proposes a unified FSOD framework based o...
Zhi-Yu Jiang, Guo-Hao Yang, Dandan Ma et al.· IEEE Transactions on Geoscie...· 0 citations
Hyperspectral image classification (HSIC) remains challenging when only a few labeled pixels are available for each class. Under such label-scarce conditions, deep models easily overfit the limited supervision and often fail to learn perturbation-invariant spatial representations from local patches. To address this iss...
Despite the success of deep learning in remote sensing (RS) image classification, substantial domain shifts—stemming from heterogeneous sensors and diverse environmental conditions—frequently compromise model reliability. Although source-free unsupervised domain adaptation (SFUDA) has emerged as a critical paradigm to...
Unknown authors· Journal of Electronic Imagin...· 0 citations
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 from the source domain. Conventional domain adaptation methods become impractical, severely restricti...
Qing-Mei Li, Juepeng Zheng, Jia-Rui Zhang et al.· 0 citations
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