The invention relates to the technical field of computers, in particular to a block chain right-confirming
forestry remote sensing data ontology
feature recognition method and
system, and the method comprises the steps: extracting the multi-scale features of
forestry remote sensing image data through a pre-trained deep
convolutional neural network, and generating a feature
pyramid with an unchanged rotation and scale; a time-space fusion
feature vector is generated by combining seasonal attention
weight adjustment, and
forestry remote sensing image fingerprints are formed after
principal component analysis and dimension reduction; a fuzzy commitment is adopted to bind the random key after
error correction coding with a forestry remote sensing image
fingerprint to generate a block chain evidence storage commitment, and a storage architecture with on-chain evidence storage and under-chain
metadata collaboration is constructed; and efficient right confirmation is realized. The problem that a traditional Hash method is sensitive to
image deformation is solved, the problem of feature drift caused by seasonal changes is solved, verifiable right confirmation is achieved, the method has the advantages of being high in
storage efficiency, high in response speed and the like, a credible ownership
authentication basis is provided for forestry remote
sensing data sharing, and the data circulation efficiency is remarkably improved.