A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze‑and‑excitation network for hyperspectral image classifcation
Küçük Resim Yok
Tarih
12.09.2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Earth Science Informatics
Erişim Hakkı
info:eu-repo/semantics/restrictedAccess
Özet
Hyperspectral image classifcation is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose signifcant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classifcation, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efciently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refnes these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, refecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classifcation accuracy with lower computational costs.
Açıklama
Anahtar Kelimeler
Depthwise separable convolution · Hyperspectral images classifcation · CNN · Deep learning · Squeeze and excitation network
Kaynak
WoS Q Değeri
Q2
Scopus Q Değeri
Cilt
Sayı
Künye
Asker, M.E., Güngör, M. A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze-and-excitation network for hyperspectral image classification. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01469-2
