Method and system for remote sensing imaging of synthetic aperture radiometer based on sparse measurement

Abstract

The invention discloses a method and system for remote sensing imaging of a synthetic aperture radiometer based on sparse measurement, wherein the method includes the steps of: establishing a sparse transformation base library, the sparse transformation base library includes a plurality of basis functions, and according to the sparse transformation base library Sparse representation of the original microwave radiation field distribution; measurement weight modeling and compression extraction of the baseline of the synthetic aperture microwave radiometer to obtain the extracted baseline; and according to the sparse representation of the original microwave radiation field A structured image inversion method is used to obtain the scene brightness temperature distribution image.

Description

technical field [0001] The present invention relates to remote sensing imaging, and more particularly to a method and system for remote sensing imaging of synthetic aperture radiometers based on sparse measurements. Background technique [0002] Microwave radiometer is a high-sensitivity receiver used to measure microwave thermal radiation of objects. It does not emit any signal, but only passively receives natural radiation signals emitted by objects. It has the advantages of small size, low power consumption, and low cost. As an important remote sensing tool, microwave radiometers can realize long-distance earth observation by using platforms such as aircraft or satellites. [0003] Real-aperture radiometers have a long history of research and have the characteristics of simple structure and stable and reliable operation. However, the antenna aperture is limited by the current manufacturing level. At the same time, strict requirements are placed on the processing accuracy....

AI Technical Summary

Problems solved by technology

[0006] High-precision remote sensing imaging applications have higher requirements for resolution and measurement sensitivity. The former requires a larger antenna array size, while the latter requires large system bandwidth and long integration time. At this time, there will be serious bottleneck constraints. :

[0007] ① The complexity of the system has risen sharply

The amount of data processed by complex correlation, on the one hand, is squarely related to the number of antennas, and on the other hand, is proportional to the system bandwidth and integration time. The overall data volume will show a massive scale, and all data processing must be completed within one imaging cycle. Dealing with a lot of stress

Images