Enhanced Satellite Image Analysis Through Pattern-Adaptive Classifier Fusion And Gradient-Hopping Hybrid Optimizer
Abstract
Satellite image examination requests necessitate precise grouping and efficient division. The unique Gradient-Hopping Hybrid Optimizer (GHHO) for satellite image segmentation is combined with a Pattern-Adaptive Classifier Fusion (PACF) for seamless classification in this study's new framework. By dynamically adapting classification methods to distinct feature sets, the concept of PACF enables adaptation to a wide range of landscapes, atmospheric conditions, and sensor specifications. By enabling precise classification of intricate patterns in satellite data, this sophisticated method enhances robustness across diverse contexts. To fragment satellite images, this cross-breed procedure provides an adaptable, versatile solution for enhancing complex capabilities. The proposed work achieves an accuracy of 95.2%, with an error rate of 0.8. Pattern-adaptive classifier fusion techniques use regularization and dropout to reduce overfitting. This streamlining agent's crossover plan, which consolidates worldwide investigation by bouncing with angle-based strategies, could help escape local minima.