SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have s...

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Bibliographic Details
Main Authors: Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang, Zongze Zhao
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
deep learning
hyperspectral image classification
attention mechanisms
convolutional neural networks
spectral-spatial learning
loss function
Online Access:https://www.mdpi.com/2072-4292/16/22/4202

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https://www.mdpi.com/2072-4292/16/22/4202

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