Advanced Spectral Fusion and Deep Learning Networks for Land use Land Cover Change Classification

Accurate detection of spatiotemporal changes in land use and land cover (LULC) is crucial for urban planning and smart city development but faces challenges due to urban environments and satellite imagery complexities. To address these, a novel “Spectral Fusion Autoencoder with Capsule and Convolutional Long Short-Term Memory Network” is proposed for enhanced land cover classification. Additionally, sub-pixel change detection faces challenges due to spectral mixing, where small urban features share similar spectral properties with surrounding environments, complicating accurate differentiation. To address this, a “Spectral Unmixing Residual Network with Variational Autoencoder” is proposed, separating mixed signals, capturing spatial features, and modeling nonlinear relationships for enhanced sub-pixel detection. Furthermore, geometric distortions in multitemporal or multi-source imagery, especially in steep terrains, disrupt spatial alignment. Thus, a novel “Dynamic Attention-Driven Capsule Policy Optimization Network” addresses these issues by refining geometric corrections and enhancing spatial consistency through reinforcement learning. Moreover, gradual spectral transitions and intra-class variability complicate inter-class land cover changes. So, a novel, “Warped Spectral Fusion Network with ConvLongShort Net” integrates spectral, spatial, and temporal data to correct illumination and viewing angle variations, capturing subtle transitions effectively. The experimental results demonstrate the proposed model’s efficiency in accurately detecting the spatiotemporal changes with an accuracy of 0.99, loss of 0.003, fractal error of 0.32, and misclassification rate of 0.06.