Yunsong Li

DaiXun Li, Weiying Xie, Yunsong Li et al.

Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources. Deep neural networks can reveal the underlying distribution of multi-modal remote sensing data, but the in-orbit fusion of multimodal data is more difficult because of the limitations of different sensor imaging characteristics, especially when the multimodal data follows non-independent identically distribution (Non-IID) distributions. To address this problem while maintaining classification performance, this paper proposes a manifold-driven multi-modality fusion framework, FedFusion, which randomly samples local data on each client to jointly estimate the prominent manifold structure of shallow features of each client and explicitly compresses the feature matrices into a low-rank subspace through cascading and additive approaches, which is used as the feature input of the subsequent classifier. Considering the physical space limitations of the satellite constellation, we developed a multimodal federated learning module designed specifically for manifold data in a deep latent space. This module achieves iterative updating of the sub-network parameters of each client through global weighted averaging, constructing a framework that can represent compact representations of each client. The proposed framework surpasses existing methods in terms of performance on three multimodal datasets, achieving a classification average accuracy of 94.35$\%$ while compressing communication costs by a factor of 4. Furthermore, extensive numerical evaluations of real-world satellite images were conducted on the orbiting edge computing architecture based on Jetson TX2 industrial modules, which demonstrated that FedFusion significantly reduced training time by 48.4 minutes (15.18%) while optimizing accuracy.}

Jitao Ma, Weiying Xie, Yunsong Li

Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. HAD is essential in scenarios of unknown or camouflaged target features, such as water quality monitoring, crop growth monitoring and camouflaged target detection, where prior information of targets is difficult to obtain. Existing HAD methods aim to objectively detect and distinguish background and anomalous spectra, which can be achieved almost effortlessly by human perception. However, the underlying processes of human visual perception are thought to be quite complex. In this paper, we analyze hyperspectral image (HSI) features under human visual perception, and transfer the solution process of HAD to the more robust feature space for the first time. Specifically, we propose a small target aware detector (STAD), which introduces saliency maps to capture HSI features closer to human visual perception. STAD not only extracts more anomalous representations, but also reduces the impact of low-confidence regions through a proposed small target filter (STF). Furthermore, considering the possibility of HAD algorithms being applied to edge devices, we propose a full connected network to convolutional network knowledge distillation strategy. It can learn the spectral and spatial features of the HSI while lightening the network. We train the network on the HAD100 training set and validate the proposed method on the HAD100 test set. Our method provides a new solution space for HAD that is closer to human visual perception with high confidence. Sufficient experiments on real HSI with multiple method comparisons demonstrate the excellent performance and unique potential of the proposed method. The code is available at https://github.com/majitao-xd/STAD-HAD.