Complicated Scene Classification Using a Deep Active Learning Paradigm for Wetland Remote Sensing Analysis

Investigating the intricate semantics of diverse wetland landscapes is vital for the development of Intelli gent computing systems within remote sensing applications. This study introduces a cutting-edge approach that utilizes memristor-based architectures to integrate multi-channel perceptual visual features for classifying wetland remote sensing images, characterized by complex spatial and ecological structures. Our method leverages a deep hierarchical model designed to emulate human gaze dynamics through a memristor-enabled processing unit, employing the BING objectness metric to accurately detect key ecological features and details across multiple scales within wetland scenes. To enhance the human-like visual attention mechanism, we propose a Memristor-Enhanced Robust Deep Active Learning (MRDAL) strategy, which systematically generates gaze shifting paths (GSPs) and extracts their deep representations using memristor-based networks. A distinctive aspect of MRDAL is its resilience against label noise, achieved through a sparse penalty mechanism embedded within the memristor architecture, effectively filtering out irrelevant GSP features. We subsequently apply a manifold-regularized feature selector (MRFS) integrated with memristive components to extract high-quality deep GSP features, which are then utilized to train a linear Support Vector Machine (SVM) for the classification of wetland scenes. Empirical evaluations reveal the method’s superior performance over conventional models, demonstrating its exceptional capability in discerning complex patterns within a comprehensive dataset of large-scale wetland remote sensing images. This advancement highlights the potential of memristor-based intelligent computing technologies for ecological monitoring and environmental analysis.