14 citations · 21 across the 14 of their papers we have counts for
14 papers · 1 filter
Burnt area extraction from high-resolution satellite images based on anomaly detection
Oscar David Rafael Narvaez Luces, Minh-Tan Pham, Quentin Poterek +1
Wildfire detection using satellite images is a widely studied task in remote sensing with many applications to fire delineation and mapping. Recently, deep learning methods have be…
Knowledge Distillation for Object Detection: from generic to remote sensing datasets
Hoàng-Ân Lê, Minh-Tan Pham
Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a…
Multimodal Object Detection in Remote Sensing
Abdelbadie Belmouhcine, Jean-Christophe Burnel, Luc Courtrai +2
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area…
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder
Minh-Tan Pham, Hugo Gangloff, Sébastien Lefèvre
This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection…
Object counting from aerial remote sensing images: application to wildlife and marine mammals
Tanya Singh, Hugo Gangloff, Minh-Tan Pham
Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques…
Joint multi-modal Self-Supervised pre-training in Remote Sensing: Application to Methane Source Classification
Paul Berg, Minh-Tan Pham, Nicolas Courty
With the current ubiquity of deep learning methods to solve computer vision and remote sensing specific tasks, the need for labelled data is growing constantly. However, in many ca…
Learning Digital Terrain Models from Point Clouds: ALS2DTM Dataset and Rasterization-based GAN
Hoàng-Ân Lê, Florent Guiotte, Minh-Tan Pham +2
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challeng…
Vehicle detection and counting from VHR satellite images: efforts and open issues
Alice Froidevaux, Andréa Julier, Agustin Lifschitz +5
Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban g…
Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks
Minh-Tan Pham, Sébastien Lefèvre
In this work, we exploit convolutional neural networks (CNNs) for the classification of very high resolution (VHR) polarimetric SAR (PolSAR) data. Due to the significant appearance…
Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding
Minh-Tan Pham
This paper presents an efficient method for texture retrieval using multiscale feature extraction and embedding based on the local extrema keypoints. The idea is to first represent…
Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study
Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre
The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) ge…
Recent Developments from Attribute Profiles for Remote Sensing Image Classification
Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula +1
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially fo…
Buried object detection from B-scan ground penetrating radar data using Faster-RCNN
Minh-Tan Pham, Sébastien Lefèvre
In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due…
Texture and Color-based Image Retrieval Using the Local Extrema Features and Riemannian Distance
Minh-Tan Pham, Grégoire Mercier, Lionel Bombrun +1
A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize…