In this paper, an automatic RNN (Auto-RNN) for HSI classification is proposed. Firstly, a number of candidate modules, including ReLU, tanh, sigmoid, and ...
An automatic RNN (Auto-RNN) for HSI classification is proposed, and a number of candidate modules, including ReLU, tanh, sigmoid, and identity, are provided ...
Notably, 2D-CNN has become a prevalent approach in hyperspectral image classification (HSIC) for capturing spatial features. ... Hyperspectral Target Detection- ...
Recurrent Neural Networks (RNN). RNNs have emerged as a dynamic and adaptable tool in HSI, offering a unique approach to analyzing the spectral dimension of ...
Dec 9, 2024 · A recurrent neural network (RNN), an important branch of the deep learning family, is mainly designed to handle sequential data. Can sequence- ...
Hyperspectral image (HSI) classification is a core task in the remote sensing community, and recently, deep learning-based methods have shown their ...
Missing: Recurrent | Show results with:Recurrent
This article proposes a spectral–spatial method for classification of hyperspectral images (HSIs) by modifying traditional Auto-Encoder based on Majorization ...
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Mar 21, 2017 · In this paper, we study the ability of RNN for hyperspectral data classification by extracting the contextual information from the data.
Oct 21, 2021 · Hyperspectral image (HSI) classification has been a hot topic for decides, as hyperspectral images have rich spatial and spectral information ...
Missing: Recurrent | Show results with:Recurrent
This paper proposes a novel RNN model that can effectively analyze hyperspectral pixels as sequential data and then determine information categories via ...
Missing: Automatic | Show results with:Automatic