Heterogeneous image change detection using Deep Canonical Correlation Analysis

Yang, Jing; Zhou, Yuan; Cao, Ying; Feng, Liyang

doi:10.1109/icpr.2018.8545885

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…To put such outcomes in context, we report that the semi-supervised method proposed by Volpi et al in [24] could reach on average a KC of 0.65 (standard deviation 0.06). The same methodology, improved by Yang et al in [54] by applying deep canonical correlation analysis, could not perform better than 0.947 (standard deviation 0.02) in OA and 0.71 (standard deviation 0.1) in KC. In [55], Roscher et al achieved a KC of 0.80 on the same dataset but by using two Landsat 5 TM images, so performing homogeneous CD.…”

Section: Resultsmentioning

confidence: 93%

Unsupervised Image Regression for Heterogeneous Change Detection

Luppino

Bianchi

Moser

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.

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Section: Resultsmentioning

confidence: 93%

Unsupervised Image Regression for Heterogeneous Change Detection

Luppino

Bianchi

Moser

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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“…According to the authors, the model focuses solely on change/no-change information, which is insufficient for some practical applications [157]. Yang et al [169] provided a unique cross-sensor CD approach based on deep canonical correlation analysis (DCCA). Following training with samples from the entire area, the DCCA transformation allows aligning the spectrum of two heterogeneous multi-spectral datasets; then, any change detection approach is used.…”

Section: Deep Learning-based Semi-supervised Methods For Vhr Imagesmentioning

confidence: 99%

“…Various methods show high performances in [169,[172][173][174][175][176]178,179]. However, there are still some limitations, as presented in Table 13.…”

Section: Deep Learning-based Unsupervised Methods For Heterogeneous I...mentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

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“…In [27], inspired by the success of DNN in representation learning, DCCA was proposed as an non-linear extension of CCA and proved to perform well in image recognition [35], cross-view feature extraction [36], and image change detection [37], [38].…”

Section: A Deep Canonical Correlation Analysismentioning

confidence: 99%

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

Ru,

Du,

2020

Preprint

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Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the land-use transitions. Existing methods for scene change detection rarely focus on the temporal correlation of bi-temporal features, and are mainly evaluated on small scale scene change detection datasets. In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings. We firstly extracts the deep representations of the bi-temporal inputs with deep convolutional networks. Then the extracted features will be projected into a lower dimension space to computed the instance-level correlation. The cross-temporal fusion will be performed based on the computed correlation in CorrFusion module. The final scene classification are obtained with softmax activation layers. In the objective function, we introduced a new formulation for calculating the temporal correlation. The detailed derivation of backpropagation gradients for the proposed module is also given in this paper. Besides, we presented a much larger scale scene change detection dataset and conducted experiments on this dataset. The experimental results demonstrated that our proposed CorrFusion module could remarkably improve the multi-temporal scene classification and scene change detection results.

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Heterogeneous image change detection using Deep Canonical Correlation Analysis

Cited by 14 publications

References 11 publications

Unsupervised Image Regression for Heterogeneous Change Detection

Unsupervised Image Regression for Heterogeneous Change Detection

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

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