Change Detection in Remote Sensing Images Using Conditional Adversarial Networks

Lebedev, Maksim; Vizilter, Yu. V.; Выголов, О. В.; Knyaz, V. A.; Rubis, A. Yu.

doi:10.5194/isprs-archives-xlii-2-565-2018

Cited by 348 publications

(187 citation statements)

References 54 publications

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“…As one of the DL-based methods, Method 5 utilized the dense skip connections within the UNet++ architecture to learn multiscale feature maps from different semantic levels. It had shown outstanding performance in terms of CD based on the satellite image pair set which was presented by Lebedev [35], and the OA could reach more than 89%. However, Method 5 showed low accuracy and bad stability in all three datasets in this study.…”

Section: General Results and Analysis Of Datasetsmentioning

confidence: 95%

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

et al. 2020

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Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote sensing (HRRS) images based on morphological attribute profiles and decision fusion is proposed in this study. By establishing the objective function based on the minimum of average interscale correlation, a morphological attribute profile with adaptive scale parameters (ASP-MAPs) is presented to exploit the spatial structure information. On this basis, a multifeature decision fusion framework based on the Dempster–Shafer (D-S) theory is constructed for obtaining the CD map. Experiments of multitemporal HRRS images from different sensors have shown that the proposed method outperforms the other advanced comparison CD methods, and the overall accuracy (OA) can reach more than 83.9%.

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Section: General Results and Analysis Of Datasetsmentioning

confidence: 95%

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

et al. 2020

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“…One is the FC-EF [62], which is an end-to-end change detection method based on the CNN and predicts changes from bi-temporal images directly. The other is a generative adversarial network (GAN)-based method [70] with the same end-to-end manner.…”

Section: Building Change Detection Resultsmentioning

confidence: 99%

“…Different from directly using image pairs as the input of the change detection network [70], we use the binary maps produced by the building extraction network as the input. This modification has tremendous advantages.…”

Section: Self-trained Building Change Detection Networkmentioning

confidence: 99%

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

et al. 2019

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We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm's robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods-including the most recent CNN-based and generative adversarial network (GAN)-based ones-have only reached 25% AP in their best cases.

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“…Lebedev proposed a specially modified generative adversarial network (GAN) architecture based on pix2pix for automatic change detection in season-varying remote sensing images. In this research, object shift was considered, which is crucial for the buildings in orthorectified images [25].…”

Section: Introductionmentioning

confidence: 99%

PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

Jiang

et al. 2020

Remote Sensing

190

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In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.

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Change Detection in Remote Sensing Images Using Conditional Adversarial Networks

Cited by 348 publications

References 54 publications

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

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