DAE-MLP based feature extraction for hyperspectral image classification of Saint Clair river
Hyperspectral remote sensing has emerged as a powerful tool for vegetation classification due to its ability to capture detailed spectral information. This study introduces a novel methodology for vegetation classification using exclusively hyperspectral imagery. The proposed approach comprises atmo...
| Authors: | ; ; |
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| Format: | Article |
| Publication date: | 2025 |
| Country: | España |
| Institution: | Universitat Autònoma de Barcelona |
| Repository: | Dipòsit Digital de Documents de la UAB |
| Language: | English |
| OAI Identifier: | oai:ddd.uab.cat:320930 |
| Online Access: | https://ddd.uab.cat/record/320930 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.1827 |
| Access Level: | Open access |
| Keyword: | Hyperspectral remote sensing FLAASH PCA SAM Multi-layer perceptron (mlp) Saint clair river |
| Summary: | Hyperspectral remote sensing has emerged as a powerful tool for vegetation classification due to its ability to capture detailed spectral information. This study introduces a novel methodology for vegetation classification using exclusively hyperspectral imagery. The proposed approach comprises atmospheric correction using the FLAASH algorithm, followed by dimensionality reduction using PCA and segmentation through the ROI selection and the Spectral Angle Mapper (SAM) module. Subsequently, a deep autoencoder is employed for feature extraction, paving the way for classification using the Multi-Layer Perceptron (MLP) algorithm. The effectiveness of this methodology is evaluated using a hyperspectral image of the Saint Clair River, successfully classifying the image into six main classes: water 1, water 2, grass, tree, reed, corn, and an 'unclassified' category encompassing concrete, roads, bricks, wood, and more. Our findings demonstrate the efficacy of this approach in accurately classifying and mapping vegetation in river ecosystems, offering a promising solution in the face of limited hyperspectral datasets. |
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