Very high resolution (VHR) remote sensing change detection (CD) is crucial for monitoring Earth’s dynamics but faces challenges in capturing fine-grained changes and distinguishing them from pseudo-changes due to varying acquisition conditions. Existing deep learning methods often suffer from information loss via downsampling, obscuring details, and lack filter adaptability to spatial heterogeneity. To address these issues, we introduce Information-Preserving Adaptive Convolutional Network (IPACN). IPACN features a novel Information-Preserving Backbone (IPB), leveraging principles adapted from reversible networks to minimize feature degradation during hierarchical bi-temporal feature extraction, enhancing the preservation of fine spatial details, essential for accurate change delineation. Crucially, IPACN incorporates a Frequency-Adaptive Difference Enhancement Module (FADEM) that applies adaptive filtering, informed by frequency analysis concepts, directly to the bi-temporal difference features. The FADEM dynamically refines change signals based on local spectral characteristics, improving discrimination. This synergistic approach, combining high-fidelity feature preservation (IPB) with adaptive difference refinement (FADEM), yields robust change representations. Comprehensive experiments on benchmark datasets demonstrate that IPACN achieves state-of-the-art performance, showing significant improvements in F1 score and IoU, enhanced boundary delineation, and improved robustness against pseudo-changes, offering an effective solution for very high resolution remote sensing CD.