Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning |
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| Authors: | S. Rajalakshmi S. Nalini Ahmed Alkhayyat Rami Q. Malik |
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| Affiliation: | 1 Department of Information Technology, Velalar College of Engineering and Technology, Erode, 638012, India2 Department of Computer Science & Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, India3 College of Technical Engineering, The Islamic University, Najaf, Iraq4 Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq |
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| Abstract: | Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC model, the synergic deep learning (SDL) model is exploited to produce feature vectors. The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs. The ISMO algorithm is used to enhance the classification efficiency of the GCN method, which is derived by integrating chaotic concepts into the SMO algorithm. The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset. |
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| Keywords: | Deep learning remote sensing images image classification slime mould optimization parameter tuning |
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