Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding

Saha, Sudipan; Bovolo, Francesca; Bruzzone, Lorenzo

doi:10.1109/tgrs.2020.3000296

Cited by 140 publications

(80 citation statements)

References 48 publications

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“…The validity and robustness of models are reflected through the detection accuracy of the changed area, the accuracy of the non-changed area, and the overall detection indexes (overall accuracy (OA), kappa, AUC, and F1). [12,94,151,157,159], respectively. The internal parameter settings are mentioned in the original literatures.…”

Section: Discussionmentioning

confidence: 99%

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A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

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Section: Discussionmentioning

confidence: 99%

“…• Noise interference: In addition to the annotated SAR data [42], the pre-processed differential data is also available to act as a criterion for variation generation in the GAN [53,159,160]. Experiences prove GAN possesses the ability to recover the real scene distribution from the noisy input.…”

Section: Dnn For Feature Generationmentioning

confidence: 99%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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“…Unsupervised image-to-image translation with adversarial learning has been drawing attention in the field of remote sensing, owing to their advantages in using unlabeled and unpaired data set for training. Recently, a cycle-consistent GAN has been exploited to transcode synthetic-aperture-radar (SAR) images into optical images for building change detection using optical-like features [17,18]. GAN architectures have also proven their advantages using discriminative features of the discriminator in hyperspectral image classification [19,20].…”

Section: Related Workmentioning

confidence: 99%

Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

et al. 2020

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We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods.

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“…Furthermore, these two parameters are sometimes combined to conduct building damage detection [20,21]. Due to the development of high-spatialresolution (HR) and very-high-spatial-resolution (VHR) SAR images, an increasing number of change detection methods have been proposed to detect building damage at the individual building level [22][23][24][25][26]. In addition, deep learning-based methods for building change detection have been proposed using VHR SAR images [25,27].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the development of high-spatialresolution (HR) and very-high-spatial-resolution (VHR) SAR images, an increasing number of change detection methods have been proposed to detect building damage at the individual building level [22][23][24][25][26]. In addition, deep learning-based methods for building change detection have been proposed using VHR SAR images [25,27]. Change detection-based methods for building damage detection have been studied adequately and used in many cases of emergency observation.…”

Section: Introductionmentioning

confidence: 99%

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

et al. 2021

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Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

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Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding

Cited by 140 publications

References 48 publications

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

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