Knowledge Distillation for Object Detection: from generic to remote sensing datasets

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 remote sensing context various off-the-shelf object detection knowledge distillation methods which have been originally developed on generic computer vision datasets such as Pascal VOC. In particular, methods covering both logit mimicking and feature imitation approaches are applied for vehicle detection using the well-known benchmarks such as xView and VEDAI datasets. Extensive experiments are performed to compare the relative performance and interrelationships of the methods. Experimental results show high variations and confirm the importance of result aggregation and cross validation on remote sensing datasets.Comment: Accepted for publishing at IGARSS 202

Studying livestock breeding wastewater treatment with bentonite adsorbent

The possibility of using adsorbents (bentonite, diatomite and kaolinite) for obtaining adsorptive materials effective in livestock breeding wastewater treatment has been assessed. It has been shown on the example of ions of ammonia (NH4) and phosphate (PO43) that particles of bentonite have relatively high adsorption capacity. The data about adsorption kinetics have been processed with the use of first and second-order kinetic models. It has been revealed that the second-order kinetic model described better adsorption of ammonia and phosphate from aqueous solutions by particles of bentonit

Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder

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 framework that computes metrics directly on the input space, enhancing interpretability and anomaly localization compared to feature embedding methods. Building upon the success of Vector-Quantized Variational Autoencoders in anomaly detection on computer vision datasets, we adapt them to the marine animal detection domain and address the challenge of handling noisy data. To evaluate our approach, we compare it with existing methods in the context of marine animal detection from aerial image data. Experiments conducted on two dedicated datasets demonstrate the superior performance of the proposed method over recent studies in the literature. Our framework offers improved interpretability and localization of anomalies, providing valuable insights for monitoring marine ecosystems and mitigating the impact of human activities on marine animals.Comment: 4 pages, accepted to IGARSS 202

Essential oil of Citrus hystrix DC.: A mini-review on chemical composition, extraction method, bioactivities, and potential applications in food and pharmaceuticals

Citrus hystrix DC. is a common herb in tropical regions. Its essential oils are now widely researched and applied because of their high economic value and safety for humans and are interesting materials for future trends. This review provides an extensive overview of the biological activities of C. hystrix essential oil, characterized predominantly by citronellal, ?-Pinene, sabinene, limonene, and terpinene-4-ol, which are deciding factors in antimicrobial, antioxidant, insect repellent, anti-tumor, and anti-inflammatory properties. Therefore, it is applied in the fields of food preservation and pharmaceuticals. However, these applications should consider the ratio of these components in the essential oil, which is variable when using materials from different parts of the plant and depending on the original location of the plant, growth stages, traditional or modern extraction methods, and pre-treatment methods

Détection d'objets enterrés par apprentissage profond sur imagerie géoradar

International audienc

The situation and efficiency of using E-learning training model at Can Tho University

Can Tho University, as a key university in the Mekong Delta region, has strived to exploit E-learning in order to create opportunities and advances to meet learning demands of students inside and outside the region. The paper, based on the university’s E-learning model, analyzes and assesses the reality and proposes some policies to develop this form of training to become one of the common practices of training in the coming time, creating more opportunities for people and communities to participate in a learning society, following the inevitable future trend

Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era

Almost Vietnamese big businesses often use outsourcing services to do marketing researches such as analysing and evaluating consumer intention and behaviour, customers’ satisfaction, customers’ loyalty, market share, market segmentation and some similar marketing studies. One of the most favourite marketing research business in Vietnam is ACNielsen and Vietnam big businesses usually plan and adjust marketing activities based on ACNielsen’s report. Belong to the limitation of budget, Vietnamese small and medium enterprises (SMEs) often do marketing researches by themselves. Among the marketing researches activities in SMEs, customer segmentation is conducted by tools such as Excel, Facebook analytics or only by simple design thinking approach to help save costs. However, these tools are no longer suitable for the age of data information explosion today. This article uses case analysing of the United Kingdom online retailer through clustering algorithm on R package. The result proves clustering method’s superiority in customer segmentation compared to the traditional method (SPSS, Excel, Facebook analytics, design thinking) which Vietnamese SMEs are using. More important, this article helps Vietnamese SMEs understand and apply clustering algorithm on R in customer segmenting on their given data set efficiently. On that basis, Vietnamese SMEs can plan marketing programs and drive their actions as contextualizing and/or personalizing their message to their customers suitabl

Aromatic character of planar boron-based clusters revisited by ring current calculations

The planarity of small boron-based clusters is the result of an interplay between geometry, electron delocalization, covalent bonding and stability. These compounds contain two different bonding patterns involving both sigma and pi delocalized bonds, and up to now, their aromaticity has been assigned mainly using the classical (4N + 2) electron count for both types of electrons. In the present study, we reexplored the aromatic feature of different types of planar boron-based clusters making use of the ring current approach. B3(+/-), B-4(2-), B-5(+/-), B-6, B-7(-), B-8(2-), B-9(-), B-10(2-), B-11(-), B-12, B-13(+), B-14(2-) and B-16(2-) are characterized by magnetic responses to be doubly sigma and pi aromatic species in which the pi aromaticity can be predicted using the (4N + 2) electron count. The triply aromatic character of B-12 and B-13(+) is confirmed. The pi electrons of B-18(2-), B-19(-) and B-20(2-) obey the disk aromaticity rule with an electronic configuration of [1 sigma(2)1 pi(4)1 delta(4)2 sigma(2)] rather than the (4N + 2) count. The double aromaticity feature is observed for boron hydride cycles including B@B5H5+, Li7B5H5 and M@BnHnq clusters from both the (4N + 2) rule and ring current maps. The double pi and sigma aromaticity in carbon-boron planar cycles B7C-, B8C, B6C2, B9C-, B8C2 and B7C3- is in conflict with the Huckel electron count. This is also the case for the ions B11C5+/- whose ring current indicators suggest that they belong to the class of double aromaticity, in which the pi electrons obey the disk aromaticity characteristics. In many clusters, the classical electron count cannot be applied, and the magnetic responses of the electron density expressed in terms of the ring current provide us with a more consistent criterion for determining their aromatic character

Multimodal Object Detection in Remote Sensing

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 focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.Comment: 4 pages, accepted to IGARSS 202

Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

International audienceThis article introduces an extension of morphological attribute profiles (APs) by extracting their local features. The so-called local feature-based attribute profiles (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e. APs' sample) within its neighborhood, hence better deal with local texture information from the image content. In this work, LFAPs are constructed by extracting some simple first-order statistical features of the local patch around each APs' sample such as mean, standard deviation, range, etc. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In addition, since the self-dual attribute profiles (SDAPs) has been proved to outperform the APs in recent years, a similar process will be applied to form the local feature-based SDAPs (LFSDAPs). In order to evaluate the effectiveness of LFAPs and LFSDAPs, supervised classification using both the Random Forest and the Support Vector Machine classifiers is performed on the very high resolution Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show that LFAPs (resp. LFSDAPs) can considerably improve the classification accuracy of the standard APs (resp. SDAPs) and the recently proposed histogram-based APs (HAPs)