Spaceborne Full Polarimetric SAR Image Classification and Information Interpretation Based on Integrated Learning View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2011-2015

FUNDING AMOUNT

600000 CNY

ABSTRACT

Despite the rapid development of remote sensing data acquisition capability of spaceborne fully polarized synthetic aperture radar (SAR), there are still many problems in the selection of image processing algorithms, precision and applicability, and generalization. Aiming at the difficulties of PolSAR image processing and the research progress of machine learning, we will introduce the integrated learning of multiple learning machine and improve the accuracy of information interpretation. We will introduce PolSAR image processing and construct the PolSAR image processing and interpretation method based on integrated learning System, the algorithm model library is established as a collection of learning machines, and the combination strategy of PolSAR polarization scattering characteristics, image statistical features and spatial structure characteristics is studied. Three typical tasks of image classification, change detection and target recognition are selected to study the key issues such as learning machine difference measure, learning machine optimization selection and integrated learning strategy in PolSAR image processing, and introduce semi-supervised learning and multi-sample learning to solve the sample The number of small and speckle noise problems. Through the land cover classification, urban expansion monitoring, disaster loss monitoring and other tests, summarized for PolSAR image classification and information extraction integrated learning strategies. The research results are of great significance to improve the reliability of PolSAR image processing, expand the theory of integration and promote the application of PolSAR data. More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=41171323

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