Subgrouping-Based NMF with Imbalanced Class Handling for Hyperspectral Image Classification

Islam, Md. Touhid; Kumar, Mohadeb; Islam, Md. Rashedul; Sohrawordi, Md.

doi:10.1109/iccit57492.2022.10055177

Cited by 4 publications

(1 citation statement)

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“…Most contemporary models designed for HSI classification predominantly adopt either a 2D-CNN structure [25] or a 3D-CNN framework [27]. However, while the 2D-CNN excels at capturing spatial information, it tends to overlook the valuable spectral intricacies inherent in HSI data [38]. On the other hand, the 3D-CNN strives to simultaneously extract both spatial and spectral information; however, this approach might not effectively extract features.…”

Section: Background Of Sennmentioning

confidence: 99%

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

Islam,

Uddin

et al. 2024

Remote Sensing

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The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

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Section: Background Of Sennmentioning

confidence: 99%