Multi-source remote sensing image radiometric normalization method

Abstract

The invention discloses a multi-source remote sensing image radiometric normalization method. The method is characterized in that relative radiometric normalization of a multi-source remote sensing image is divided into a sensor radiation correction process and a radiometric normalization process specific to external factors such as illumination. The method comprises the following steps: S1, acquiring a sensor radiation correction coefficient in a categorical regression way based on a clear sky image; S2, implementing semi-automatic classification and sensor radiation deviation correction of the multi-source image by a sample transmitting and reclassifying method; and S3, implementing relative radiometric normalization of the image through a PIFs (Pseudo Invariant Features) automatic selection method based on an NDVI (Normalized Difference Vegetation Index) histogram of differences and a category constraint. Through adoption of the multi-source remote sensing image radiometric normalization method, radiation deviations among sensors are effectively corrected, and higher radiometric normalization accuracy is achieved as a whole than a conventional method. Meanwhile, radiation feature fluctuation among time-sequence images can be eliminated effectively through the method, so that aspect change information of land such as vegetative can be expressed more accurately, and a reference method is provided for cooperative utilization of the multi-source time-sequence images.

Description

technical field [0001] The invention relates to the technical field of remote sensing images, in particular to a radiation normalization method for multi-source remote sensing images. Background technique [0002] The acquisition of remote sensing images is affected by factors such as the sensor itself, illumination, atmosphere, terrain, etc., resulting in great differences in the spectral characteristics of the same ground object on different images. Therefore, before using multi-source or multi-temporal remote sensing images for change detection or feature information extraction, it is necessary to perform radiation normalization processing on the images to control and reduce the variation of the surface landscape caused by differences in lighting conditions, atmospheric effects, and sensor responses. "Pseudo-change" retains real surface change information. [0003] There are two types of radiation normalization: absolute radiation normalization and relative radiation nor...

AI Technical Summary

Problems solved by technology

For multi-source time-series images with high observation frequency, it is necessary to effectively improve the accuracy and automation level of radiometric normalization while considering the spectral, radiometric and geometric resolution differences among multi-source images to meet its application requirements. The key point; most of the current radiation normalization methods focus on the research of homogeneous and finite time-phase data, and the degree of automation is low, and there are few methods for radiation normalization of multi-source time series data

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