Image Change Detection Based on Fuzzy Clustering and Neural Networks

In the change detection of synthetic aperture radar images, the image quality and change detection accuracy are difficult to meet the application requirements due to the influence of speckle noise. Therefore, the study improved the fuzzy C-means algorithm by introducing fuzzy membership degree and Gabor texture features. Features were weighted through channel attention, resulting in an image change detection model, namely, the fuzzy local information C-means for Gabor textures and multi-scale channel attention wavelet convolutional neural network. The segmentation accuracy of the model was 0.995, which improved by 0.119 compared to the traditional fuzzy C-means algorithm. When adding multiplicative noise with different variances, the noise variance reached 0.30, and the accuracy of the algorithm still reached 0.982. In practical application analysis, the detection and segmentation accuracy of river images was 0.983 with a partition coefficient of 0.935, and the segmentation accuracy of farmland images was 0.960 with a partition coefficient of 0.902. Therefore, the algorithm has good stability and anti-noise performance. The algorithm can be widely applied in various fields of synthetic aperture radar image change detection, such as disaster assessment, urban development monitoring, and environmental change monitoring. This paper provides more accurate analysis results, which help with policy formulation and effective resource management.