A Novel Remote Sensing Recognition Using Modified GMM Segmentation and DenseNet

The accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response and environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, and high computational demands often hinder the performance of traditional methods. To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction methods, optimization algorithms, and deep learning. Our approach begins with novel Graph-cut Optimized Fuzzy GMM Segmentation (GC-GMM), ensuring precise object identification and boundary delineation. We employ Azimuthal Average Feature Extraction, Haar Wavelet Transform, and Maximally Stable Extremal Regions (MSER) to capture a rich set of features encompassing texture, frequency, and shape information. These features are fused and refined using Particle Swarm Optimization (PSO) to create a robust and informative representation. Leveraging the power of deep learning, a DenseNet architecture achieves superior classification accuracy based on the optimized feature set. This framework effectively tackles the limitations of previous methods by combining the strengths of diverse feature extraction techniques with deep learning capabilities. The use of optimization algorithms further enhances the discriminative power of the features, leading to improved classification accuracy.