http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108257151-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10044 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30181 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2135 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-246 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-462 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-46 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-246 |
| filingDate | 2017-12-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2019-08-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2019-08-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-108257151-B |
| titleOfInvention | PCANet image change detection method based on saliency analysis |
| abstract | The invention discloses a PCANet image change detection method based on significance analysis. It mainly solves the problems of too many training samples caused by the existing Automatic PCANet method, too long processing time and the influence of the scattering noise of the SAR image on the classification result. The implementation steps are: obtain the difference map of the two-temporal SAR image; analyze the significance of the difference map; use the threshold method to extract the salient area, and then use the threshold method to classify the positive samples, negative samples and uncertain pixels; use PCANet to extract positive samples. , negative samples, and features corresponding to uncertain pixels, use the features corresponding to positive and negative samples to train the support vector machine SVM module, and then input the features of uncertain pixels into SVM for final classification. Compared with the existing Automatic PCANet method, the invention has high detection precision and operation efficiency, good anti-noise performance, and can be used for SAR image change detection. |
| priorityDate | 2017-12-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419580923 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID3001055 |
Total number of triples: 20.