To exploit the spatial and spectral prior, we design a spatial-spectral block (SSB), which consists of a spatial residual module and a spectral attention residual module. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered...
The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-...
Learning Exhaustive Correlation for Spectral SR :SpatialSpectral Attention Meets Linear Dependence Haoqian Wang RGB/HSI restoration1 人赞同了该文章 两个创新策略: 【结构1】 【本质1】 QKV之前,就是在token划分上做文章 【解释1】 首先,现有的Transformer通常分别强调空间相关性或光谱相关性,破坏了HSI的三维特...
To alleviate the high computation costs in the aforementioned methods, deep-learning algorithm has been proposed as an alternative for learning spatial–spectral prior and spectral reconstruction. Deep-learning techniques can perform faster and more accurate reconstruction than iterative approaches, thus is...
【论文翻译】Spectral-Spatial Residual Network for Hyperspectral Image Classification A 3-D Deep Learning F,程序员大本营,技术文章内容聚合第一站。
Fig. 4: Description of off-axis holography based on spatial spectral filtering. FT Fourier transform, IFT inverse Fourier transform Full size image Both the in-line and off-axis holography discussed above are lensless, where the sensor and sample planes are not mutually conjugated. Therefore, a...
In this letter, a new deep learning framework for spectral–spatial classification of hyperspectral images is presented. The proposed framework serves as an engine for merging the spatial and spectral features via suitable deep learning architecture: stacked autoencoders (SAEs) and deep convolutional ne...
Brain functionality classification Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment RNN, 3D-CNN IEEE Trans. Neural Syst. Rehabil. Eng. 2018 Brain functionality classification Learning temporal information for brain-compu...
Hyperspectral remote sensing has a strong ability in information expression, so it provides better support for classification. The methods proposed to deal
A common seizure detection approach is the use of 2D-CNNs to classify the time-frequency maps of univariate EEG signals. However, we aim to incorporate all dimensions – spectral, spatial, and temporal – into one model. Classifying univariate signals ignores the locations of electrodes over the...