A method for detecting the change of urban surface features in remote sensing image based on Siamese convolution network

Abstract

The invention provides a method for detecting the change of urban surface features in remote sensing image based on Siamese convolution network, the Siamese convolution network is a twin convolution neural network SCNN, Based on the data enhancement technique, the initial sample set is selected from the registered two-phase city images, the twin convolution neural network (SCNN) is set up, the twin convolution neural network (SCNN) is trained based on the initial sample set, and the initial sample set is expanded by the data enhancement technique. The twin convolution neural network (SCNN) istrained based on the expanded sample set, and the trained SCNN model is obtained to detect the change of urban terrain. The invention realizes the expansion of the sample through the data enhancementtechnology, and designs a Siamese convolution neural network, avoids the tedious steps of artificially designed features in the traditional change detection method, and realizes the end-to-end operation; Fully considering the spatial attributes of the image, the accuracy and reliability of the change detection are improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image change detection, in particular to a change detection method of urban ground objects. Background technique [0002] Change detection is the process of using two or more remote sensing images acquired at different time points in the same geographical area to discover the changes that have occurred on the earth's surface. Change detection is an important means to maintain the current situation of geographic information data, and it is an important research direction in the field of remote sensing applications. In recent years, my country's urbanization process has been accelerating, and urban features are changing with each passing day. The change detection of urban ground features plays an important role in grasping the law of urban change, updating urban maps, assisting urban planning and design, and government decision-making. [0003] Traditional change detection methods need to m...

AI Technical Summary

Problems solved by technology

However, it is difficult to apply this technology to the field of change detection.

The reason is that two or more remote sensing images acquired at different time points in the same geographical area have great differences due to different shooting times, climate conditions, light conditions, and shooting angles. Simply using grayscale transformation, rotation, arbitrary cropping, and color Operations such as dithering cannot increase sample diversity

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