Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification
Autor: | Nur Shafira Nisa Shaharum, M. M. A. Al-Habshi, M. A. A. M. Abidin, Helmi Zulhaidi Mohd Shafri |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | IOP Conference Series: Earth and Environmental Science. 767:012019 |
ISSN: | 1755-1315 1755-1307 |
DOI: | 10.1088/1755-1315/767/1/012019 |
Popis: | This study aims to evaluate the performance of state-of-the-art HybridSN deep learning algorithm versus standard machine learning (ML) and deep learning (DL) techniques using open-source Python libraries for producing hyperspectral land use and land cover (LULC) classification maps. Japanese Chikusei hyperspectral datasets captured by the airborne platform using Hyperspec-VNIR-C sensor were used in this study. Standard ML methods used in this study were support vector machine linear kernel (SVM-linear), support vector machine radial basis function kernel (SVM-RBF) and random forests (RFs) that were provided in Python’s Scikit-learn library. DL techniques used in this study were multilayer perceptron (MLP), two-dimensional convolutional neural network (2-D CNN) and hybrid spectral convolutional neural network (HybridSN), which integrates the 2-D and 3-D feature learning. These DL models were built based on the sequential model using Keras API. The results show that all the proposed methods obtained overall accuracies (OAs) above 95%. The HybridSN and 2-D CNN models gave the best score with 99.97% OAs for hyperspectral image classification using the Chikusei dataset. |
Databáze: | OpenAIRE |
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