Multi-sensor change detection based on nonlinear canonical correlations

Volpi, Michele; Morsier, Frank de; Camps‐Valls, Gustau; Kanevski, Mikhaïl; Tuia, Devis

doi:10.1109/igarss.2013.6723187

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…In the settings presented in this paper, the method is able to properly highlight land cover changes in a pair of images from a same sensor. Similarly, in our previous work (Volpi et al, 2013a) the kernel CCA was studied for the alignment of data spaces in a pair of images from a single sensor. To study the robustness of the proposed method in cross-sensor situations, some channels were removed from one of the acquisitions considered.…”

Section: Introductionmentioning

confidence: 98%

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

Volpi

Camps‐Valls

Tuia

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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125

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Section: Introductionmentioning

confidence: 98%

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

Volpi

Camps‐Valls

Tuia

2015

ISPRS Journal of Photogrammetry and Remote Sensing

Self Cite

125

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“…Multi-sensor SoA CD methods define a common feature domain to compare the features retrieved by the various data. The multi-sensor CD is performed by i) normalizing the input multi-sensor data to define a common feature domain, 19,20 ii) retrieving common-domain features using feature extraction and selection approaches (e.g., Principal Component Analysis), 21,22 using DL method to iii) define a common feature domain 23,24 or iv) adapt the input data domain. 25 In Ref.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised change detection in multi-modal SAR images using CycleGAN

Bergamasco

Bovolo²

2022

Image and Signal Processing for Remote Sensing XXVIII

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Many Change Detection (CD) methods exploit the bi-temporal multi-modal data derived by multiple sensors to find the changes effectively. State-of-the-Art CD methods define features with a common domain between the multi-modal data by normalizing input images or ad hoc feature extraction/selection methods. Deep Learning (DL) CD methods automatically learn features with a common domain during the training or adapt the features derived by multi-modal data. However, CD methods focusing on multi-sensor multi-frequency SAR data are still poorly investigated. We propose a DL CD method that exploits a Cycle Generative Adversarial Network (CycleGAN) to automatically learn and extract multi-scale feature maps in a domain common to the input multifrequency multi-sensor SAR data. The feature maps are learned, during unsupervised training, by generators that aim to transform the input data domain into the target one while preserving the semantic information and aligning the feature domain. We process the multi-sensor multi-frequency SAR data with the trained generators to produce bi-temporal multi-scale feature maps that are compared to enhance changes. A standard-deviation-based feature selection is applied to keep only the most informative comparisons and reject the ones with poor change information. The multi-scale comparisons are used for a detail preserving CD. Preliminary experimental results conducted on bi-temporal SAR data acquired by Cosmo-SkyMed and SAOCOM on the urban area of Milan, Italy, in January 2020 and August 2021 provided promising results.

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“…Thirdly, one can also identify algorithms based mainly on a projection or transformation of the bi-temporal heterogeneous images into a common representation space, in which the pair of multimodal satellite images have the same behavior in statistical terms and on which conventional change detection techniques using homogeneous multitemporal satellite images can then be adopted [31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

MRF Models Based on a Neighborhood Adaptive Class Conditional Likelihood For Multimodal Change Detection

Mignotte¹

2022

ACRT

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Statistical methods for automatic change detection, in heterogeneous bitemporal satellite images, remains a challenging research topic in remote sensing mainly because this research field involves the processing of image data with potentially very different statistical behaviors. In this paper, we propose a new Bayesian statistical approach, relying on spatially adaptive class conditional likelihoods which are also adaptive to the considered imaging modality pair and whose parameters are estimated in a first preliminary estimation step. Once that estimation is done, a second stage is dedicated to the change detection segmentation itself based on this likelihood model defined for each pixel and for each imaging modality. In this context, we compare and discuss the performance of different Markovian segmentation strategies obtained in the sense of several non-hierarchical or hierarchical Markovian estimators on real satellite images with different imaging multi-modalities. Based on our original pixel-wise likelihood model, we also compare these Markovian segmentation strategies over the existing state-of-the-art heterogeneous change detection algorithms proposed in the literature.

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Multi-sensor change detection based on nonlinear canonical correlations

Cited by 12 publications

References 9 publications

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

Unsupervised change detection in multi-modal SAR images using CycleGAN

MRF Models Based on a Neighborhood Adaptive Class Conditional Likelihood For Multimodal Change Detection

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