Seydi, S.T.; Boueshagh, M.; Namjoo, F.; Minouei, S.M.; Nikraftar, Z.; Amani, M. A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module. Remote Sens.2024, 16, 827.
Seydi, S.T.; Boueshagh, M.; Namjoo, F.; Minouei, S.M.; Nikraftar, Z.; Amani, M. A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module. Remote Sens. 2024, 16, 827.
Seydi, S.T.; Boueshagh, M.; Namjoo, F.; Minouei, S.M.; Nikraftar, Z.; Amani, M. A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module. Remote Sens.2024, 16, 827.
Seydi, S.T.; Boueshagh, M.; Namjoo, F.; Minouei, S.M.; Nikraftar, Z.; Amani, M. A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module. Remote Sens. 2024, 16, 827.
Abstract
Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.
Keywords
Land cover analysis; remote sensing; change detection; hyperspectral; deep learning; convolutional neural networks (CNN); Squeeze-and-Excitation (SE); AVIRIS
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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