Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• 2022 25th International Conference on Computer and Information Technology (ICCIT), 2022, p.739-744

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• The remote sensing industry is actively discussing the classification of hyperspectral images (HSIs). For the first time, the idea of subgrouping dimensionality is presented using a modified deep learning model, and this research presents a novel framework for dimensionality reduction in HSI classification as a result. In particular, our system uses the subgrouping model to extract many characteristics from a dataset and then apply a selection criterion. First, we performed data reduction and subgrouping by extracting the correlation matrix. After that, we resample the data and use it as input for a hyperspectral picture classification. In the proposed framework, we combine NMF on spectral dimensions with information-based feature selection and a wavelet-based 2D CNN on spatial dimensions to classify spectral-spatial data. Based on the experimental findings, it is clear that this framework delivers the most excellent classification accuracy compared to other approaches, including traditional classifiers like PCA and MNF-based deep learning methods.