Interval type-2 fuzzy neural network for remote sensing image segmentation
This study is dedicated to tackling the fuzziness and uncertainty inherent in remote sensing image segmentation,presenting a novel approach for the task.The method integrates mean and variance fuzziness to enhance the model's robustness,employs a hinge loss function to simplify computations,and introduces a new optimization algorithm that combines genetic algorithms with constraint-based optimization techniques to boost model performance and accuracy.The evaluation was conducted using multiple sets of remote sensing data with varying spectral and spatial resolutions,calculating several validity indicators for detailed comparative analysis.In experiments involving the classification of golf courses and harbor features,the overall accuracy improved by an average of 17.05％and 6.5％,respectively,compared to advanced methods such as interval type-2 fuzzy neural networks.This approach effectively addresses the fuzziness and uncertainty in remote sensing image segmentation,paving the way for new research avenues in the field.
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