Practical framework and methodology for high-performance intelligent invariant detection in remote sensing imagery
In addressing the challenges posed by sample category imbalance,limited algorithm applicability,and inadequate knowledge application inherent in traditional change detection techniques,we propose a novel framework for high-reliability,intelligent invariant detection of land classes in remote sensing imagery.This framework employs advanced algorithms to pre-cisely extract stable invariant areas that are typically irrelevant to various tasks,thereby reducing the operational footprint and boosting productivity in practical settings.Commencing with data preprocessing,a sample library tailored to the specifics of in-variant detection is developed.Additionally,we introduce a method for invariant detection that utilizes prior information to guide the discrimination between global and local pseudo-changes.This approach leads to the creation of a gridded invariant mask and the introduction of two object-level metrics-compression accuracy and compression range-to assess the framework's performance in terms of accuracy and efficiency.Empirical validation across multiple national regions confirms that this frame-work not only minimizes the workload associated with manual visual interpretation but also significantly improves the efficiency of data extraction,thus offering a groundbreaking solution for extracting change information from remote sensing data in real-world scenarios.
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