Change detection based on Faster R-CNN for high-resolution remote sensing images

Wang, Qing; Zhang, Xiao‐Dong; Chen, Guanzhou; Fan, Deng-Ping; Gong, Yue; Zhu, Kun

doi:10.1080/2150704x.2018.1492172

Cited by 108 publications

(54 citation statements)

References 23 publications

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“…In [10], the authors propose a supervised change detection architecture based on based on U-Nets [11]. Similarly, in [12], the authors propose another and better supervised architectures based on convolutional neural networks (CNN) and that shows very good performance to separate trivial changes from non-trivial ones. This issue of detecting non-trivial changes is also a problem that we tackle in our proposed method, but in addition to these two algorithms from the state of the art, we do it using unsupervised learning thus alleviating the cost of manually labeling data.…”

Section: Supervised Methods For Change Detection and Damage Mappingmentioning

confidence: 99%

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

2019

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Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.

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Section: Supervised Methods For Change Detection and Damage Mappingmentioning

confidence: 99%

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

2019

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“…These aimed at understanding how the different streams of data (pre-and post-event) could be merged together for an optimal image classification of façade damages. The merging of the different epochs/modalities of data within a CNN has been the focus of many recent research in change detection (Daudt et al, 2018;Wang et al, 2018), but also in merging multi-modal data (Audebert et al, 2018;Xu et al, 2017). Two distinct approaches are tested: early and late fusion of the epoch-specific streams.…”

Section: Multi-temporal Approachesmentioning

confidence: 99%

Damage Detection on Building Façades Using Multi-Temporal Aerial Oblique Imagery

Duarte

Nex

Kerle

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Over the past decades, a special interest has been given to remote-sensing imagery to automate the detection of damaged buildings. Given the large areas it may cover and the possibility of automation of the damage detection process, when comparing with lengthy and costly ground observations. Currently, most image-based damage detection approaches rely on Convolutional Neural Networks (CNN). These are used to determine if a given image patch shows damage or not in a binary classification approach. However, such approaches are often trained using image samples containing only debris and rubble piles. Since such approaches often aim at detecting partial or totally collapsed buildings from remote-sensing imagery. Hence, such approaches might not be applicable when the aim is to detect façade damages. This is due to the fact that façade damages also include spalling, cracks and other small signs of damage. Only a few studies focus their damage analysis on the façade and a multi-temporal approach is still missing. In this paper, a multi-temporal approach specifically designed for the image classification of façade damages is presented. To this end, three multi-temporal approaches are compared with two mono-temporal approaches. Regarding the multi-temporal approaches the objective is to understand the optimal fusion between the two imagery epochs within a CNN. The results show that the multi-temporal approaches outperform the mono-temporal ones by up to 22% in accuracy.</p>

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“…With the development of computer sciences, some studies have started to apply deep learning technique to process remote sensing images [57][58][59]. Because of the unique character of remote sensing images, some deep learning techniques such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been proven to generate a higher performance [60][61][62]. Nevertheless, there are some parts that need to be improved in future research.…”

Section: Limitations About Remote Sensing Images and Algorithmsmentioning

confidence: 99%

Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques

2019

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Disasters are an unpredictable way to change land use and land cover. Improving the accuracy of mapping a disaster area at different time is an essential step to analyze the relationship between human activity and environment. The goals of this study were to test the performance of different processing procedures and examine the effect of adding normalized difference vegetation index (NDVI) as an additional classification feature for mapping land cover changes due to a disaster. Using Landsat ETM+ and OLI images of the Bento Rodrigues mine tailing disaster area, we created two datasets, one with six bands, and the other one with six bands plus the NDVI. We used support vector machine (SVM) and decision tree (DT) algorithms to build classifier models and validated models performance using 10-fold cross-validation, resulting in accuracies higher than 90%. The processed results indicated that the accuracy could reach or exceed 80%, and the support vector machine had a better performance than the decision tree. We also calculated each land cover type’s sensitivity (true positive rate) and found that Agriculture, Forest and Mine sites had higher values but Bareland and Water had lower values. Then, we visualized land cover maps in 2000 and 2017 and found out the Mine sites areas have been expanded about twice of the size, but Forest decreased 12.43%. Our findings showed that it is feasible to create a training data pool and use machine learning algorithms to classify a different year’s Landsat products and NDVI can improve the vegetation covered land classification. Furthermore, this approach can provide a venue to analyze land pattern change in a disaster area over time.

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Change detection based on Faster R-CNN for high-resolution remote sensing images

Cited by 108 publications

References 23 publications

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Damage Detection on Building Façades Using Multi-Temporal Aerial Oblique Imagery

Spatial–Temporal Analysis of Land Cover Change at the Bento Rodrigues Dam Disaster Area Using Machine Learning Techniques

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