Ontological Feature Recognition and Blockchain-based Rights Confirmation Method for Forestry Remote Sensing Data

Against the backdrop of smart forestry development, forestry data exhibits characteristics of being massive, multi-source, heterogeneous, strongly interrelated, and privacy-sensitive. Existing data applications face issues like unclear inter-departmental ownership, information silos, and cross-domain collaboration barriers . This study proposes an ontological feature recognition and blockchain-based rights confirmation method for forestry remote sensing data, addressing challenges such as difficult data rights confirmation, low sharing efficiency, and privacy leakage risks. Methodologically, the deep learning model ResNet is employed to extract multi-scale semantic features from remote sensing images, which are then dimensionally reduced via Principal Component Analysis (PCA) to generate a "data fingerprint" with rotation and scaling invariance, serving as an intrinsic identifier for data rights confirmation. The blockchain-based rights confirmation framework integrates the Fuzzy Commitment theory to bind fingerprints with error-correcting codes for on-chain commitment generation, adopting a hybrid query mechanism of "off-chain block localization + on-chain verification" to address the sensitivity of traditional hashing methods to image transformations and the inefficiency of pure on-chain queries . Experimental results show that query latency can be controlled within 100 ms when the feature dimension d ≤ 512, validating the real-time performance and efficiency of the method in high-dimensional feature matching. The study confirms that this solution enables efficient rights confirmation and secure sharing of forestry remote sensing data through content-aware fingerprint binding and blockchain immutability, providing a technological pathway to break through data governance bottlenecks in smart forestry.