High-resolution remote sensing image change detection method based on local invariant features

Abstract

The invention discloses a high-resolution remote sensing image change detection method based on local invariant features. On the basis of two times of cross partitioning of an image pair to be detected, the similarity confidence of image block pairs is analyzed using LBP and SURF feature descriptors, the change property of 1/4 image blocks is judged based on the similarity confidence, and block effects formed by changing and non-changing 1/4 image blocks are processed in a morphology regional growth way. Two times of partitioning are carried out, the texture features are analyzed according to image blocks, the image change property is judged according to 1/4 image blocks, and the accuracy of image block description and the precision of image block analysis are improved. By making use of the local invariance and light insensitivity of LBP and SURF features, the similarity between image blocks can be judged more accurately. Through morphological growth, the complexity of regional growth threshold selection is reduced, and processing of holes and other discontinuous small regions is avoided.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, belongs to multi-temporal remote sensing image change detection based on local invariant features, and in particular relates to a high-resolution remote sensing image change detection method based on LBP and SURF features. Background technique [0002] Remote sensing images have the characteristics of large coverage and intuitive reflection of the surface. Multi-temporal remote sensing image change detection is widely used in land monitoring, environmental monitoring, disaster monitoring, and urban planning. [0003] Remote sensing image change detection methods are mainly divided into pixel-based and object-based methods, and the features used mainly include grayscale and texture. The object-based method needs to segment the image to extract the analysis object, but there is a problem of segmentation scale in image segmentation, and there is a problem of feature selection in the...

AI Technical Summary

Problems solved by technology

[0005] In recent years, the spatial resolution of remote sensing images has become higher and higher, up to the decimeter level, and the geometric and texture details of objects in high-resolution remote sensing images are better presented in the image, but the grayscale of the image is affected by climate, Factors such as light intensity affect lower-resolution remote sensing images greatly, which brings challenges to high-resolution remote sensing image change detection

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