http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-104408466-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V30-194 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2014-11-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2017-10-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2017-10-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-104408466-B |
| titleOfInvention | Semi-supervised classification method for hyperspectral remote sensing images based on local manifold learning composition |
| abstract | The invention discloses a hyperspectral remote sensing image semi-supervised classification method based on local manifold learning composition. The realization steps are: (1) preparing a training sample set, including a small amount of labeled data and a large amount of unlabeled data; (2) Based on the distance measurement method of spectral angle mapping, select k nearest neighbor points for each sample point in the training sample set; use the local manifold learning algorithm to obtain the weights between the connection points in the graph structure, and calculate the graph adjacency matrix , to get the corresponding graph structure; based on the graph adjacency matrix, based on the GFHF algorithm to classify the unlabeled data; (3) use the generalization algorithm of GFHF to classify other data points in the image. The invention connects two widely used algorithms, the local manifold learning dimensionality reduction algorithm and the semi-supervised classification algorithm, through a "graph", and shows good applicability to the classification of various hyperspectral remote sensing data, and can significantly improve the Classification Accuracy of Spectral Remote Sensing Images. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108614926-A |
| priorityDate | 2014-11-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 15.