دورية أكاديمية
A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module
| العنوان: | A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module |
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| المؤلفون: | Seyd Teymoor Seydi, Mahboubeh Boueshagh, Foad Namjoo, Seyed Mohammad Minouei, Zahir Nikraftar, Meisam Amani |
| المصدر: | Remote Sensing, Vol 16, Iss 5, p 827 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science |
| مصطلحات موضوعية: | land cover analysis, remote sensing, change detection, hyperspectral, deep learning, convolutional neural networks (CNN), Science |
| الوصف: | Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 16050827 2072-4292 |
| Relation: | https://www.mdpi.com/2072-4292/16/5/827; https://doaj.org/toc/2072-4292 |
| DOI: | 10.3390/rs16050827 |
| URL الوصول: | https://doaj.org/article/653be3f3582e4738b72f8ca698367d5b |
| رقم الأكسشن: | edsdoj.653be3f3582e4738b72f8ca698367d5b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16050827 20724292 |
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| DOI: | 10.3390/rs16050827 |