A new difference image creation method based on deep neural networks for change detection in remote-sensing images

Cao, Guo; Wang, Bisheng; Haro-Carrión, Xavier; Yang, Di; Southworth, Jane

doi:10.1080/01431161.2017.1371861

Cited by 35 publications

(21 citation statements)

References 24 publications

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“…Moreover, another commonly used unsupervised scheme is based on the latent change map, as shown in Figure 8b. In addition to the pre-trained model obtained by transfer learning, it can be generated by an unsupervised AI model (e.g., AEs), and the final change map is then generated by using a clustering algorithm [23,79,98,107,157,163,184].…”

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

“…However, its units within the same layer are not connected to each other and each hidden layer serves as the visible layer for the next. As a feature extractor, it can be trained greedily, i.e., one layer at a time, and appears in many unsupervised change detection methods [23,37,157,183]. On the other hand, the deep Boltzmann machine (DBM), as a graph similar to DBN but undirected, can also achieve such a function [182].…”

Section: Deep Belief Networkmentioning

confidence: 99%

“…The training and testing processes are performed alternately and iteratively. During the training process, the model is optimized according to a learning criterion, which can be a loss function in deep learning (e.g., softmax loss [21], contrastive loss [22], Euclidean loss [23], or cross-entropy loss [24]). By monitoring the training process and test accuracy, the convergence state of the AI model can be obtained, which can help in adjusting its hyperparameters (such as the learning rate), and also in judging whether the model performance has reached the best (i.e., termination) condition; 4.…”

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confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

Section: Deep Belief Networkmentioning

confidence: 99%

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confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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“…At the same time, automatic methods for selection of changed and unchanged pixels are used to obtain training samples for a multiple classifier system [13]. Following this paper, the authors of [1] propose the improved backpropagation method of a deep belief network (DBN) for change detection based on automatically selected change labels.…”

Section: Related Workmentioning

confidence: 99%

Change Detection in Satellite Images Using Reconstruction Errors of Joint Autoencoders

Kalinicheva

Sublime

Trocan

2019

Lecture Notes in Computer Science

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With the growing number of open source satellite image time series, such as SPOT or Sentinel-2, the number of potential change detection applications is increasing every year. However, due to the image quality and resolution, the change detection process is a challenge nowadays. In this work, we propose an approach that uses the reconstruction losses of joint autoencoders to detect non-trivial changes (permanent changes and seasonal changes that do not follow common tendency) between two co-registered images in a satellite image time series. The autoencoder aims to learn a transformation model that reconstructs one co-registered image from another. Since trivial changes such as changes in luminosity or seasonal changes between two dates have a tendency to repeat in different areas of the image, their transformation model can be easily learned. However, non-trivial changes tend to be unique and can not be correctly translated from one date to another, hence an elevated reconstruction error where there is change. In this work, we compare two models in order to find the most performing one. The proposed approach is completely unsupervised and gives promising results for an open source time series when compared with other concurrent methods.

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“…Gong et al [17] uses generative adversarial network to update DI until convergence. In [18], a new DI building method is proposed in which an improved backpropagation algorithm highlights the difference between unchanged class and changed class. In general, after obtaining DI, most works will analyse DI and identify changed area of the whole image.…”

Section: Introductionmentioning

confidence: 99%

SAR image change detection based on deep denoising and CNN

Cao

Wang

et al. 2019

IET Image Processing

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A new difference image creation method based on deep neural networks for change detection in remote-sensing images

Cited by 35 publications

References 24 publications

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection in Satellite Images Using Reconstruction Errors of Joint Autoencoders

SAR image change detection based on deep denoising and CNN

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