Polarized SAR image classification method on basis of SAE and IDL

Abstract

The invention discloses a polarized SAR (Synthetic Aperture Radar) image classification method on the basis of an SAE (Stake Auto-Encoder) and IDL (Inbalanced Data Learning). The polarized SAR image classification method comprises the following implementing steps: (1) preprocessing; (2) training parameters of the SAE; (3) extracting characteristics; (4) training parameters of a Softmax classifier; (5) acquiring a classification result I; (6) establishing an IDL model; (7) training the IDL model; (8) acquiring a classification result II; (9) outputting a final classification result. The polarized SAR image classification method adopts the SAE to extract the characteristics capable of more substantially describing an original input; moreover, the polarized SAR image classification method solves the problem of large classification accuracy difference between classes, which is caused by class unbalance of a training sample set, and has the advantage of improving classification accuracy and region consistency; and the polarized SAR image classification method can be applied to the fields of terrain classification, target detection, identification and the like of a remote sensing image.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a polarization synthetic aperture radar (Synthetic Aperture) based on stacked auto-encoder (Stake Auto-Encoder, SAE) and unbalanced data learning (Inbalanced Data Learning, IDL) in the technical field of target recognition Radar, SAR) image classification method. The invention can be applied to the target recognition of the polarimetric SAR image, and can accurately classify different areas of the polarimetric SAR image. Background technique [0002] Polarimetric SAR image classification is an important branch in the field of remote sensing image processing, so many scholars have proposed different solutions to this problem. According to the utilization of polarization information, the classification methods of polarimetric SAR images can be divided into methods based on polarization scattering characteristics, methods based on polarization statistical cha...

AI Technical Summary

Problems solved by technology

Compared with other polarimetric synthetic aperture radar SAR image classification techniques in the prior art, the present invention has low computational complexity, and can well overcome the low overall classification accuracy caused by the imbalance between training sample classes, or the The problem of large gaps in classification accuracy improves classification accuracy and regional consistency

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