Optical Remote Sensing Change Detection Through Deep Siamese Network

Arabi, Mahdi; Karoui, Moussa Sofiane; Djerriri, Khelifa

doi:10.1109/igarss.2018.8518178

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…Traditional CD methods PBCD Bruzzone et al [7], Celik [8], Deng et al [9], Wu et al [10], Huang et al [11], Benedek et al [14], and Bazi et al [18] OBCD Ma et al [20], Zhang et al [21], Gil-Yepes et al [22], Qin et al [23] Deep learning CD methods FB-DLCD Sakurada et al [26], Saha et al [27], Hou et al [28], El Amin et al [29], Zhan et al [31], Zhang et al [32], Niu et al [33], and Zhan et al [34] PB-DLCD Gong et al [36], Arabi et al [38], Ma et al [40], Zhang et al [41], Khan et al [42], Daudt et al [44], Wang et al [45], Wiratama et al [46], Zhang et al [47], Mou et al [49], and Gong et al [50] IB-DLCD Lei et al [52], Daudy et al [53], Lebedev et al [54], and Guo et al [55] To address the above-mentioned issues, we proposed a novel end-to-end method based on improved UNet++ [58], which is an effective encoder-decoder architecture for semantic segmentation. A novel loss function was designed and an effective deep supervision (DS) strategy was implemented, which are capable of capturing changes with varying sizes effectively in complex scenes.…”

Section: Category Example Studiesmentioning

confidence: 99%

“…In [37], a refined DI is obtained based on the generative adversarial network (GAN), where the joint distribution of the image patches and training data are fed into the network for training. Siamese CNN architectures are widely utilized in the PB-DLCD for its effective feature fusion and representation abilities [38,39]. In addition, such PB-DLCD strategy was also adopted for multi-modality images or incomplete images, such as heterogeneous Synthetic Aperture Radar (SAR) images [40], laser scanning point clouds and 2-D imagery [41], and incomplete satellite images [42].…”

Section: Introductionmentioning

confidence: 99%

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End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

2019

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Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

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Section: Category Example Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

2019

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“…Specifically, the most commonly used types of multispectral images for AI-based change detection methods are derived from the Landsat series of satellites and the Sentinel series of satellites [66,67], due to their low acquisition cost and high time and space coverage. In addition, other satellites, such as Quickbird [68][69][70][71][72][73][74], SPOT series [75][76][77][78], Gaofen series [14,79,80], Worldview series [81][82][83][84][85], provide high and very high spatial resolution images, and various aircrafts provide very high spatial resolution aerial images [20,[86][87][88][89][90][91][92][93][94], allowing the change detection results to retain more details of the changes. HSIs have hundreds or even thousands of continuous and narrow bands, which can provide abundant spectral and spatial information.…”

Section: Optical Rs Imagesmentioning

confidence: 99%

“…According to whether the weights of sub-networks are shared, this can be divided into the pure-Siamese structure [22,68,94,117,155,156] and the pseudo-Siamese structure [79,109,157,158]. The main difference is that the former sub-network extracts the common features of the two-period data by sharing weights.…”

Section: Siamese Structurementioning

confidence: 99%

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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“…Semantic segmentation tasks in deep learning were mostly based on fully convolutional networks (FCNs) [28], which could classify each pixel in the image independently and quickly. Zhang [29] and Arabi [30] proved that FCN methods could be well applied to image change detection tasks. However, these works only optimized the structure of the original model; features of image change detection were not improved.…”

Section: Introductionmentioning

confidence: 99%

TCDNet: Trilateral Change Detection Network for Google Earth Image

et al. 2020

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Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.

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Optical Remote Sensing Change Detection Through Deep Siamese Network

Cited by 20 publications

References 12 publications

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

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

TCDNet: Trilateral Change Detection Network for Google Earth Image

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