Remote sensing image change detection method based on CVA and sample selection

Abstract

The invention discloses a remote sensing image change detection method based on CVA (Change Vector Analysis) and sample selection, which comprises the following steps: registering two remote sensing images of different time phases in a detection area; segmenting the remote sensing images at multiple scales to get image disks; integrating the features of all the image disks to get a difference image; gridding the difference image to get multiple difference image blocks, and calculating the standard deviation; generating a sorting curve according to the difference degree from big to small, and extracting the difference image blocks at the position with maximum slope change of the curve and before the position with maximum slope change; acquiring a change threshold based on a Bayesian threshold, and segmenting the difference image in a binary manner according to the change threshold, and getting a change detection result; and outputting the detection result. The method has the following advantages: the multi-dimensional features of an image object are integrated, the ability of different features of the image object in change detection is displayed, and the reliability and application scope of the change detection algorithm are improved; and the change detection algorithm is very stable.

Description

technical field [0001] The invention relates to the field of remote sensing science and technology, and specifically designs a remote sensing image change detection method based on CVA (Change Vector Analysis, change vector analysis) and sample selection. Background technique [0002] Due to the increasing application of high-resolution remote sensing images, object-level change detection, which can take advantage of its advantages, has gradually replaced pixel-level change detection and become the mainstream change detection technology. The object is a homogeneous collection of pixels, which can also be called an image spot. The image spot contains multi-dimensional features, and the change detection method based on the difference method or the ratio method uses a single feature for change detection, which cannot fully express the information of the image spot. [0003] Unsupervised change detection can be used to quickly detect changed regions. The key to realize unsuperv...

AI Technical Summary

Problems solved by technology

However, geographical national conditions monitoring covers a large geographical area and requires the use of large-format remote sensing images. When the time interval between the two time-phase remote sensing images is short, the ratio of the area of ​​the changed area to the unchanged area in the detection area is very small.

Directly using the Bayesian threshold determination method based on the EM algorithm cannot obtain accurate change thresholds

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