Abstract: Image processing algorithms continue to demand higher performance from computers. However, computer performance is not improving at the same rate as before. In response to the current ...

Abstract: With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the ...

Abstract: Integration of complementary information from different modalities and efficient computation is crucial in remote sensing (RS) image classification applications. Convolutional neural ...

Abstract: Nowadays, foundation models have demonstrated exceptional performance across numerous downstream tasks. However, the effective application of these models to hyperspectral image ...

Abstract: Efficient extraction of spectral sequences and geospatial information is crucial in hyperspectral image (HSI) classification. Recurrent neural networks (RNNs) and Transformers excel in ...

Abstract: In the Image Aesthetics Computing (IAC) field, most prior methods leveraged the off-the-shelf backbones pre-trained on the large-scale ImageNet database. While these pre-trained backbones ...

Abstract: Industrial time series prediction (ITSP) is critical to the predictive maintenance system of modern industry. However, time-varying conditions and complex industrial processes cause the ...

Abstract: Remote sensing image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral data and a low-resolution image rich in ...

Abstract: Modeling large-scale scenes from multi-view images is challenging due to the trade-off dilemma between visual quality and computational cost. Existing NeRF-based methods have made ...

Abstract: Remote sensing super-resolution (SR) technique, which aims to generate high-resolution image with rich spatial details from its low-resolution counterpart, play a vital role in many ...

Abstract: Currently, hybrid network models combining convolutional neural networks (CNNs) and Transformers have garnered significant interest among researchers in hyperspectral image (HSI) ...

Abstract: In this article, an adaptive prescribed-time neural controller is developed for the tracking problem of a class of high-order nonlinear systems with full-state constraints. First, a ...