Toward Generalized Change Detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning

Kerner, Hannah; Wagstaff, Kiri L.; Bue, Brian D.; Gray, Patrick C.; Fraeman, A. A.; Amor, Heni Ben

doi:10.1109/jstars.2019.2936771

Cited by 35 publications

(27 citation statements)

References 71 publications

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“…In the deep feature learning phase, the AI model is usually supervised, pre-trained with sufficient labeled samples in other domain data [67,110,168]. The fine-tuning phase is optional and, in this phase, only a small number of labeled samples are required for fine-tuning [125,[169][170][171][172] or additional classifier training [90,140]. Therefore, the change map can be directly obtained by the trained classifier.…”

Section: Transfer Learning-based Structurementioning

confidence: 99%

“…The commonly used AE models are stacked AEs [97,98,104], stacked denoising AEs [16,101,106,[121][122][123]151,160,188], stacked fisher AEs [189], sparse AEs [80], denoising AEs [102], fuzzy AEs [105], and contractive AEs [99,103]. These AEs preserve spatial information by expanding pixel neighborhoods into vectors, while convolutional AEs are implemented directly through convolution kernels [170,190]. According to its characteristics, AEs can be used to implement change detection in an unsupervised manner and perform well.…”

Section: Autoencodermentioning

confidence: 99%

“…Planetary surfaces Satellite images [170] Urbanization is a significant factor causing land surface change. As a result of population growth, the expansion of urbanization plays an important role in transforming natural land cover into urban facilities for people.…”

Section: Astronomymentioning

confidence: 99%

“…Many change detection techniques focus on applications that are closely related to people's daily lives, but in [170], a new AI-based approach is proposed for planetary surface change detection, employing a transfer learning-based framework and convolutional AE models. The experiments showed that the proposed method was superior to a difference image-based method.…”

Section: Astronomymentioning

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: Transfer Learning-based Structurementioning

confidence: 99%

Section: Autoencodermentioning

confidence: 99%

Section: Astronomymentioning

confidence: 99%

Section: Astronomymentioning

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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“…Finally, the transfer learning-based structure has been recently investigated to alleviate the lack of training samples and optimize the training process in a semi-supervised scenario. Transfer learning uses training in one domain to enable better results in another domain and, specifically, the lower to midlevel features learned in the original domain can be transferred as useful features in the new domain performing a fine-tuning according to a few labelled samples (Kerner et al, 2019;Larabi et al, 2019) 3 Preliminary concepts A MS/HS sensor records reflected light in tens (MS)/hundreds (HS) of narrow frequencies covering the visible, near-infrared and shortwave infrared bands of a wavelength λ (also called spectrum). The spectrum is an M-dimensional feature vector (spectral feature vector), so that λ is spanned on M numeric spectral features (bands) λ 1 , λ 2 , .…”

Section: Related Workmentioning

confidence: 99%

Leveraging autoencoders in change vector analysis of optical satellite images

et al. 2021

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Various applications in remote sensing demand automatic detection of changes in optical satellite images of the same scene acquired over time. This paper investigates how to leverage autoencoders in change vector analysis, in order to better delineate possible changes in a couple of co-registered, optical satellite images. Let us consider both a primary image and a secondary image acquired over time in the same scene. First an autoencoder artificial neural network is trained on the primary image. Then the reconstruction of both images is restored via the trained autoencoder so that the spectral angle distance can be computed pixelwise on the reconstructed data vectors. Finally, a threshold algorithm is used to automatically separate the foreground changed pixels from the unchanged background. The assessment of the proposed method is performed in three couples of benchmark hyperspectral images using different criteria, such as overall accuracy, missed alarms and false alarms. In addition, the method supplies promising results in the analysis of a couple of multispectral images of the burned area in the Majella National Park (Italy).

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Change Detection in SAR Images Using Deep Learning Methods

Bergamasco

Bovolo²

2022

Synthetic Aperture Radar (SAR) Data Applications

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Toward Generalized Change Detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning

Cited by 35 publications

References 71 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

Leveraging autoencoders in change vector analysis of optical satellite images

Change Detection in SAR Images Using Deep Learning Methods

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