A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification

By Wenju Wang, Shuguang Dou, Zhongmin Jiang, Liujie Sun. Remote Sensing, 2018

356 citations recorded by the source corpus.

Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accuracy, in this paper we propose an end-to-end fast dense spectral–spatial convolution (FDSSC) framework for HSI classification. The FDSSC framework uses different convolutional kernel sizes to extract spectral and spatial features separately, and the “valid” convolution method to reduce the high dimensions. Densely-connected structures—the input of each convolution consisting of the output of all previous convolution layers—was used for deep learning of features, leading to extremely accurate classification. To increase speed and prevent overfitting, the FDSSC framework uses a dynamic learning rate, parametric rectified linear units, batch normalization, and dropout layers. These attributes enable the FDSSC framework to achieve accuracy within as few as 80 epochs. The experimental results show that with the Indian Pines, Kennedy Space Center, and University of Pavia datasets, the proposed FDSSC framework achieved state-of-the-art performance compared with existing deep-learning-based methods while significantly reducing the training time.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/50fe48616ceff28319061f49b4aae8c76db1cd49

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Citations in the corpus

A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, background): “Similarly, CNNs with dense connections have also been introduced into this field [62, 63].”
Deep learning classifiers for hyperspectral imaging: A review (2019, methodology): “For instance, Paoletti et al. (2018a) implement a Deep&Dense CNN model for spectral-spatial classification of HSI data, while Wang et al. (2018a) analyze spectral, spatial and spectral-spatial DenseNets for HSI classification.”
Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification (2022, background): “In [44], an end-to-end dense convolutional network framework was proposed for the extraction of spatial–spectral features.”
Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network (2019, methodology): “Unlike traditional pixel-based methods that only use spectral features, 3D-cube-based methods like SSRN [31], FDSSC [32], DBMA [34], and our proposed framework exploit both spectral and spatial information.”
CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification (2020, methodology): “Besides, the residual and dense connections have also been introduced to HSI classiﬁcation [33], [34].”