Abstract: The combination of the spectral and spatial features is received wide attention in hyperspectral image (HSI) classification.
May 9, 2018 · In this paper, we propose a spectral–spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification.
Experiments demonstrate that the proposed multiscale spectral-spatial unified network with two-branch architecture for hyperspectral image classification ...
The multi-scale strategy stands out as an effective approach to enhance the accuracy of HSI classification [34][35] [36] . Pooja et al. [37] utilized a multi- ...
In this paper, we propose a spectral-spatial unified network (SSUN) with an end-to-end architecture for the hyperspectral image (HSI) classification.
This paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model ( ...
This framework is designed to establish a foundational solution for pixel-level classification tasks in Remote Sensing (RS) imagery using diverse data sources.
The CapsNet adopts vector neuron and encode the spatial relationship of features in an image, which exhibits encouraging performance. Motivated by CapsNet, this ...
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A band grouping-based long short-term memory model and a multiscale convolutional neural network are proposed as the spectral and spatial feature extractors ...
A multi-task learning spectral-spatial multiscale residual network (SSMRN) is proposed to learn features of objects effectively. In the implementation of the ...
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