See my publications from  Google Scholar and from Research Gate

Peer-reviewed Journals

[21] P. Berg, B. Uzun,  M.T. Pham, N. Courty, Multimodal Supervised Contrastive Learning in Remote Sensing Downstream Tasks",  IEEE Geoscience and Remote Sensing Letters (GRSL), 2024. [paper]

[20] M. Hamzaoui, L. Chapel, M.T. Pham, S. Lefèvre, Hyperbolic Prototypical Network for Few Shot Remote Sensing Scene Classification, Pattern Recognition Letters, 2023. [paper]

[19] P. Berg,  M.T. Pham, N. Courty, Self-Supervised Learning for Scene Classification in Remote Sensing: Current State of the Art and Perspectives, MDPI Remote Sensing, 2022. [paper]

[18] H.A. Le, H. Zhang,  M.T. Pham, S. Lefèvre, Mutual Guidance meets Contrastive Learning: Vehicle Detection in Remote Sensing Images, MDPI Remote Sensing, 2022. [paper]

[17] H.A. Le, F. Guiotte,  M.T. Pham, S. Lefèvre, T. Corpetti, Learning digital terrain models from point clouds: ALS2DTM dataset and rasterization-based GAN, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), 2022. [paper]

[16] D. Santana-Maia,  M.T. Pham,  S. Lefèvre, Watershed-based attribute profiles with semantic prior knowledge for remote sensing image analysis, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), 2022. [paper]

[15] Paul Berg, D. Santana-Maia,  M.T. Pham, S. Lefèvre, Weakly supervised detection of marine animals in high resolution aerial images, MDPI Remote Sensing, 2022. [paper]

[14] D. Santana-Maia,  M.T. Pham, F. Guiotte, E. Aptoula, S. Lefèvre, Classification of remote sensing data with morphological attributes profiles: a decade of advances, IEEE Geosci. Remote Sens. Magazines (GRSM), 2021. [paper]

[13] L. Courtrai, M.T. Pham, S. Lefèvre, Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks, Remote Sensing, 2020, 12(19), 3152. [https://www.mdpi.com/2072-4292/12/19/3152]

[12] M.T. Pham, L. Courtrai, C. Friguet, S. Lefèvre, A. Baussard, YOLO-fine: one-stage detector of small objects under various backgrounds in remote sensing images, Remote Sensing, 2020, 12(15), 2501. [https://www.mdpi.com/2072-4292/12/15/2501]

[11] F. Guiotte, M.T. Pham, R. Dambreville, T. Corpetti, S. Lefèvre, Semantic segmentation of LiDAR point clouds: Rasterization beyond Digital Elevation Models, IEEE Geosci. Remote Sens. Lett. (GRSL), 2020, early access at [https://ieeexplore.ieee.org/document/8954886]

[10] M.T. Pham, Fusion of polarimetric features and structural gradient tensors for VHR PolSAR image classification, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol 11 (10), 3732-3742,  2018. [paper]

[9] M.T. Pham, S. Lefèvre, F. Merciol, Attribute profiles on derived textural features for highly-textured optical image classification, IEEE Geosci. Remote Sens. Lett. (GRSL), vol 15(7), 1125-1129, 2018. [paper]

[8] M.T. Pham, E. Aptoula, S. Lefèvre, Feature profiles from attribute filtering for remote sensing image classification, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol 11(1), pp. 249-256, 2018. [paper]

[7] M.T. Pham, S. Lefèvre, E. Aptoula, Local feature-based attribute profiles for optical remote sensing image classification,  IEEE Trans. Geosci. and Remote Sens. (TGRS), vol 56(2), pp. 1199-1212, 2018. [paper]

[6] M.T. Pham, G. Mercier, L. Bombrun, Color texture image retrieval based on local extrema features and riemannian distance, Journal of Imaging, 2017, 3(4), 43. [paper]

[5] M.T. Pham, G. Mercier, O. Regniers, J. Michel, Texture retrieval from VHR optical remote sensed images using the local extrema descriptors with application to vineyard parcel detection, Remote Sensing, 2016, 8(5), 368. [paper]

[4] M.T. Pham, G. Mercier, J. Michel, PW-COG: an effective texture descriptor for VHR satellite imagery using a pointwise approach on covariance matrix of oriented gradients, IEEE Trans. Geosci. and Remote Sens. (TGRS), vol. 54(6), pp. 3345-3359, 2016. [paper]

[3] M.T. Pham, G. Mercier, J. Michel, Change detection between SAR images using a pointwise approach and graph theory,  IEEE Trans. Geosci. and Remote Sens. (TGRS), vol. 54(4), pp. 2020-2032, 2015. [paper]

[2] M.T. Pham, G. Mercier, J. Michel, Pointwise graph-based local texture characterization for very high resolution multispectral image classification, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol. 8(5), pp. 1962-1973, 2015. [paper]

[1] M.T. Pham, G. Mercier, J. Michel, Textural features from wavelets on graphs for very high resolution panchromatic Pléiades image classification, The French Journal of Photogrammetry and Remote Sensing (RFPT), n. 208, pp. 131-136, 2014. [paper]

Conference Papers 

[43] A.K. Duong, H.A. Le, M.T. Pham, Leveraging feature communication in federated learning for remote sensing image classification, IGARSS 2024.

[42] T.V. La, M.T. Pham, M. Chini, Insight into the collocation of multi-source satellite imagery for multi-scale vessel detection, IGARSS 2024.

[41] H.H. Nguyen, C.N. Nguyen, X.T. Dao, Q.T. Duong, T.K.D. Pham, M.T. Pham, Variational Autoencoder for Anomaly Detection: A Comparative Study, IEEE ICCE 2024.

[40] P. Berg, M.T. Pham, N. Courty, Apprentissage contrastif multi-modal : Du pré-entrainement auto-supervisé à la classification supervisée, RFIAP 2024.

[39] S. Pande, B. Uzun, F. Guiotte, M.T. Pham, T. Corpetti, F. Delerue, S. Lefèvre, Plant detection from ultra high resolution remote sensing images: A semantic segmentation approach based on fuzzy loss, IGARSS 2024.

[38] B. Uzun, S. Pande, G. Cachin-Bernard, M.T. Pham, S. Lefèvre, R. Blatrix, D. McKey, Mapping Earth mounds from space, IGARSS 2024.

[37] H. A. Le, M.T. Pham, Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets, IGARSS 2024.

[36] H. A. Le, M.T. Pham, Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data, Bristish Machine Vision Conference (BMVC) 2023.

[35] H. A. Le, M.T. Pham, Self-training and multi-task learning for limited data: an evaluation study, ICCV Workshop on Representation Learning with Very Limited Images (LIMIT) 2023. 

[34] O. Narvaez, M.T. Pham, Q. Potereck, R. Braun, Burnt area extraction from high-resolution satellite images based on anomaly detection, ECML/PKDD Workshop on Machine Learning for Earth Observation (MACLEAN) 2023. 

[33] H. Gangloff, M.T. Pham, L. Courtrai, S. Lefèvre, Unsupervised anomaly detection using variational autoencoder with gaussian random field prior, International Conference on Imag Processing (ICIP), 2023. 

[32] C. Bonet, P. Berg, N. Courty, F. Septier, L. Drumetz, M.T. Pham, Spherical sliced-wassertstein, accepted to International Conference on Representation Learning (ICLR) 2023.

[31] M.T. Pham, H. Gangloff, S. Lefèvre, Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder, IGARSS 2023.

[30] M. Hamzaoui, L. Chapel, M.T. Pham, S. Lefèvre, Hyperbolic variational auto-encoder for remote sensing scene embedding, IGARSS 2023.

[29] A. Belmouhcine, J. C. Burnel, H. Zhang, L. Courtrai, M.T. Pham, S. Lefèvre, Multimodal object detection in remote sensing, IGARSS 2023.

[28] P. Berg, M.T. Pham, N. Courty, Joint multi-modal self-supervised pre-training in remote sensing: application to methane source classification, IGARSS 2023.

[27] H. A. Le, M.T. Pham, Knowledge distillation for object detection: from generic to remote sensing datasets, IGARSS 2023.

[26] T. Singh, H. Gangloff, M.T. Pham, Object counting from aerial remote sensing images: application to wildlife and marine mammals, IGARSS 2023.

[25] H. Gangloff, M.T. Pham, L. Courtrai, S. Lefèvre, Autoencodeurs variationnels à registre de vecteurs pour la détection d'anomalies, RFIAP 2022.

[24] M. Hamzaoui, L. Chapel,  M.T. Pham, S. Lefèvre, A hierarchical prototypical network for few-shot remote sensing image classification, ICPRAI 2022. 

[23] H. Gangloff, M.T. Pham, L. Courtrai, S. Lefèvre, Leveraging vector-quantized variational autoencoder inner metrics for anamoly detection, International Conference on Pattern Recognition (ICPR), 2022. 

[23] M. Hamzaoui, L. Chapel,  M.T. Pham, S. Lefèvre, Hyperbolic Variational Auto-Encoder for Remote Sensing Scene Classification, ORASIS 2021. 

[22] D. Santana Maia, M.T. Pham, S. Lefèvre, Watershed-based attribute profiles for pixel classification of remote sensing data, International Conference on Discrete Geometry and Mathematical Morphology (DGMM). Springer, Cham, 2021. 

[21] M.T. Pham, S. Lefèvre, Very high resolution airborne PolSAR image classification using convolutional neural networks, 13th European Conference on Synthetic Aperture Radar (EUSAR), pp. 1-4. VDE, 2021. Preprinted at [https://arxiv.org/abs/1910.14578]

[20] L. Courtrai, M.T. Pham, J. C. Burnel, S. Lefèvre, Apprentissage de réseaux de neurones de super-résolution pour la détection d'objets de petite taille dans les images de télédétection, RFIAP 2020.

[19] L. Courtrai, M.T. Pham, C. Friguet, S. Lefèvre, Small object detection from remote sensing images with the help of object-focused super-resolution using Wasserstein GANs, IGARSS 2020.

[18] D. Pirrone,  M.T. Pham, A compound polarimetric-textural approach for unsupervised change detection in multi-temporal full-pol SAR imagery, IGARSS 2020.

[17] A. Froidevaux, A. Julier, A. Lifschitz, M.T. Pham, R. Dambreville, S. Lefèvre, P. Lassallle, T.-L. Huynh, Vehicle detection and counting from VHR satellite images: efforts and open issues, IGARSS 2020 [https://arxiv.org/abs/1910.10017]

[16] A. Osio, M.T. Pham, S. Lefèvre, Spatial processing of Sentinel Imagery for Monitoring of Acacia Forest Degradation in Lake Nakuru Riparian Reserve, XXIVth ISPRS Congress 2020.

[15] F. Merciol, M.T. Pham, D. Santana Maia, A. Masse, C. Sannier, Broceliande: a comparative study of attribute profiles and feature profiles from different attributes, XXIVth ISPRS Congress 2020.

[14] M.T. Pham, Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding, 2018 ECCV Workshop (accepted for Oral+poster) [paper]

[13] M.T. Pham, S. Lefèvre, Détection d'objets enterrés par apprentissage profond sur imagerie géoradar, 2018 RFIAP [paper]

[12] M.T. Pham, E. Aptoula,  S. Lefèvre, Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study, 2018 IEEE IGARSS, pp 4515-4518 [paper]

[11] M.T. Pham, S. Lefèvre, Buried object detection from B-scan ground penetrating radar data using Faster-RCNN, 2018 IEEE IGARSS, pp. 6809-6811. [paper]

[10] M.T. Pham, S. Lefèvre, E. Aptoula, L. Bruzzone, Recent developments from attribute profiles for remote sensing image classification, 2018 ICPRAI [paper]

[9] M.T. Pham, G. Mercier, E. Trouvé, S. Lefèvre, SAR image texture tracking using  pointwise graph-based model for glacier displacement measurement, 2017 IEEE IGARSS , pp. 1083-1086  [paper] [slides]

[8] M.T. Pham, S. Lefèvre, E. Aptoula, B. B. Damodaran, Classification of VHR remote sensing images using local feature-based attribute profiles, 2017 IEEE IGARSS, pp. 747-750, [paper] [slides]

[7] E. Aptoula, M.T. Pham, S. Lefèvre, Quasi-flat zones for angular data simplification, 2017 ISMM, pp. 342-354, Springer. [paper] [poster]

[6] M.T. Pham, G. Mercier, O. Regniers, L. Bombrun, J. Michel, Texture retrieval from very high resolution remote sensing images using the local extrema-based descriptors, 2016 IEEE IGARSS, pp. 1839-1842. [paper] [slides]

[5] M.T. Pham, G. Mercier, J. Michel, Pointwise approach on covariance matrix of oriented gradients for very high resolution image texture segmentation, 2015 IEEE IGARSS, pp. 1008-1011. [paper] [slides]

[4] M.T. Pham, G. Mercier, J. Michel, Covariance-based texture description from weighted coherence matrix and gradient tensors for polarimetric SAR image classification, 2015 IEEE IGARSS, pp. 2469-2472. [paper] [slides]

[3] M.T. Pham, G. Mercier, J. Michel, A keypoint approach for change detection between SAR images based on graph theory, 2015 IEEE MULTITEMP, pp. 1-4. [paper] [slides]

[2] M.T. Pham, G. Mercier, J. Michel, Wavelets on graphs for very high resolution multispectral image texture segmentation, 2014 IEEE IGARSS, pp. 2273-2276. [paper] [slides]

[1] M.T. Pham, D. Guériot, Guided block-matching for sonar image registration using unsupervised Kohonen neural networks, 2013 IEEE OCEANS'13, pp. 1-5. [paper]  [slides]

Chapters:

[2] M.T. Pham, G. Mercier. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images. Change Detection and Image Time Series Analysis 1 : Unsupervised Methods (2021) : 167-200, Wiley Online Library.

[1] S. Lefèvre., L. Courtrai, M.T. Pham, C. Friguet, J. C. Burnel (2021). Observation de la mer par apprentissage profond : quelques exemples d’applications pour protéger notre bien commun.

Conferences/workshops without proceedings

[4]  T.V. La, M.T. Pham, M. Chini, Collocation of multi-source satellite imagery for ship detection based on Deep Learning models, EGU 2024.

[3] Q. Hamard, M.T. Pham, K. Heerah, D. Cazau,  Deep learning for marine mammal monitoring from underwater acoustic data at offshore windfarm scale, CWW  2023.

[2] A. Viain, S.  Michel, G. Duclos, P.  Allain, S.  Lefèvre, M.T. Pham, K. Heerah, T. Rouyer,  SEMMACAPE: aerial survey of the marine megafauna in offshore windfarms by automatic characterisation, CWW 2023.

[1] H.A. Le,  M.T. Pham, S. Lefèvre, K. Heerah, D. Cazau, Performance of off-the-shelf computer vision methods on spectrogram-based marine mammal detection, DLDCE Workshop 2022.

Theses:

M.T. Pham, Pointwise approach for texture analysis and characterization from very high resolution images, PhD. Thesis, 09/2016, [https://tel.archives-ouvertes.fr/tel-01464333]

M.T. Pham, Study of the joint behavior of 2D/3D descriptors for heterogeneous registration of LiDAR point clouds and optical images, Master thesis, 09/2013

See my publications from Google Scholar and from Research Gate