Abstract
Abstract. We present a method for change detection in images using Conditional Adversarial Network approach. The original network architecture based on pix2pix is proposed and evaluated for difference map creation. The paper address three types of experiments: change detection in synthetic images without objects relative shift, change detection in synthetic images with small relative shift of objects, and change detection in real season-varying remote sensing images.
Highlights
Change detection in the time-varying sequences of remote sensing images acquired on the same geographical area is an important part of many practical applications, e.g. urban development analysis, environmental inspection, agricultural monitoring
The best results in the overwhelming majority of image analysis and processing tasks are delivered by methods based on deep convolutional neural networks (CNN)
We propose a new method for automatic change detection in season-varying remote sensing images, which employs such a modern type of CNN as Conditional Adversarial Networks
Summary
Change detection in the time-varying sequences of remote sensing images acquired on the same geographical area is an important part of many practical applications, e.g. urban development analysis, environmental inspection, agricultural monitoring. In most cases, solving the change detection task in manual mode is a highly time-consuming operation, which makes an automation of this process an important and practically demanded filed of research. The best results in the overwhelming majority of image analysis and processing tasks are delivered by methods based on deep convolutional neural networks (CNN). We propose a new method for automatic change detection in season-varying remote sensing images, which employs such a modern type of CNN as Conditional Adversarial Networks
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