Multi-source Image Change Detection Method for Cluster-Guided Deep Neural Network Classification

Abstract

The invention discloses a multi-source image change detection method based on clustering guided deep neural network classification, which avoids the step of difference figure generation in the early stage in traditional change detection and overcomes the defect that difference figure generation is needed in multi-source image change detection. The method comprises the following steps: inputting a gray matrix of an optical image; carrying out fuzzy clustering on the optical image to get a segmented gray matrix; marking the optical image after clustering segmentation; sampling the optical image and a TM image; selecting a training sample from the TM image; training a stacked sparse automatic encoder SAE; using a tag to fine-adjust network parameters; inputting the TM image to a network, and outputting a classified image; working out the log ratio of the two classified images; and getting a change detection result. The link of difference figure construction is abandoned. The method is applicable to multi-source remote sensing image change detection, and has the advantages of little noise influence, high precision of change detection result classification, and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, mainly relates to the combination of deep neural network and remote sensing image processing field, mainly solves the problem of change detection of remote sensing images, and specifically provides a multi-source image change detection method guided by clustering deep neural network classification, which is used for The change detection of multi-source images is widely used in aerospace, ground object coverage and utilization, earthquake disaster detection and evaluation and other fields. Background technique [0002] The continuous development of computer digital image processing, pattern recognition, artificial intelligence, and sensor data fusion technology provides more technical support for automatic change detection of remote sensing images. In the past two decades, the change detection methods of remote sensing images have been continuously updated, the change detection technology...

AI Technical Summary

Problems solved by technology

For the multi-source image change detection method acquired by different sensors, also known as multi-source image processing, scholars from various countries have studied a large number of change detection methods and theoretical models from different perspectives. Traditional methods such as algebraic methods, time series analysis methods, etc. , according to the image difference method or ratio method to generate a difference map, and then select the threshold. The algorithm of this type of method is simple and easy to implement, and some transformation methods can effectively reduce the dimension. The disadvantage is that it is difficult to overcome due to atmospheric conditions, sensor noise and The interference caused by the difference of atmospheric radiation affects the final detection results

With the deepening of the application of change detection methods, the accuracy of the detection results of the change detection method that first generates the difference map for the images obtained by different sensors is difficult to meet the current image processing requirements, and it is difficult to adapt to the current human production needs.

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