Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

Zhan, Yang; Fu, Kun; Yan, Menglong; Sun, Xian; Wang, Hongqi; Qiu, Xiaosong

doi:10.1109/lgrs.2017.2738149

Cited by 466 publications

(295 citation statements)

References 15 publications

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“…The inputs of the DSMS-FCN are two whole multi-temporal VHR images, and the output is a change map. Unlike our unsupervised architecture and majority of recent patch-based approach [18], the DSMS-FCN is able to process images of any size and do not require sliding patch-window, therefore the accuracy and speed of inference could be significantly improved.…”

Section: B Supervised Change Detectionmentioning

confidence: 99%

“…The main differences between image pairs are new builtup regions, fresh plough-land and groundwork before building over. As a public data set, ACD has already been used in [18], [19], [38], [39].…”

Section: Supervised Change Detection Experiments a Acd Data Setmentioning

confidence: 99%

“…First, to augment avalibale training data, all possible filps and rotations of multiple of 90 degree are used. For the data set split, we adopt the way was proposed in [18] and [19]: the top-left 784x448 corner of the Szada-1 and Tiszadob-3 are cropped for testing, and the rest of the images are used for training. The test image pairs is shown in Figure 12.…”

Section: B Experiments Settingsmentioning

confidence: 99%

“…The methods used for comparison are DSCN [18], CXM [38], SCCN [17] and three fully convolutional networks proposed in [19], using the values in [18] and [19]. The three comparative full-convolution networks are FC-EF, FC-Siam-Conc, FC-Siam-Diff.…”

Section: B Experiments Settingsmentioning

confidence: 99%

“…In [17], a deep symmetric network is proposed for change detection of VHR heterogeneous images, and a convolutional layer plays an important role in feature extraction. Yang et al [18] introduce a deep Siamese convolutional network for aerial image change detection, which extract features by two weight sharing convolutional branches, and generate binary change map based on the feature difference of the last layer. Three fully convolutional Siamese networks are first proposed in [19], which are trained end-to-end on change detection data set and have achieved good performances.…”

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confidence: 99%

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Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

Chen

et al. 2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Very high resolution (VHR) images provide abundant ground details and spatial distribution information. Change detection in multi-temporal VHR images plays a significant role in urban expansion and area internal change analysis. Nevertheless, traditional change detection methods can neither take full advantage of spatial context information nor cope with the complex internal heterogeneity of VHR images. In this paper, we propose a powerful multi-scale feature convolution unit (MFCU) for change detection in VHR images. The proposed unit is able to extract multi-scale features in the same layer. Based on the proposed unit, two novel deep Siamese convolutional networks, deep Siamese multi-scale convolutional network (DSMS-CN) and deep Siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection in multi-temporal VHR images. For unsupervised change detection, we implement automatic pre-classification to obtain training patch samples, and the DSMS-CN fits the statistical distribution of changed and unchanged area from patch samples through multi-scale feature extraction module and deep Siamese architecture. For supervised change detection, the end-to-end deep fully convolutional network DSMS-FCN is trained in any size of multi-temporal VHR images, and directly output the binary change map. In addition, for the purpose of solving the inaccurate localization problem, the fully connected conditional random field (FC-CRF) is combined with DSMS-FCN to refine the results. The experimental results with challenging data sets confirm that the two proposed architectures perform better than the state-of-the-art methods.

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Section: B Supervised Change Detectionmentioning

confidence: 99%

Section: Supervised Change Detection Experiments a Acd Data Setmentioning

confidence: 99%

Section: B Experiments Settingsmentioning

confidence: 99%

Section: B Experiments Settingsmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

Chen

et al. 2019

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Recognition in Terra Incognita

Beery

Horn

Perona

2018

Lecture Notes in Computer Science

468

402

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It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. 1

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AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images

et al. 2019

Lecture Notes in Computer Science

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Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images

Cited by 466 publications

References 15 publications

Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-temporal VHR Images

Recognition in Terra Incognita

AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images

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