Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use

Molecular epidemiology of white spot syndrome virus within Vietnam

White spot syndrome virus (WSSV), the sole member of the virus family Nimaviridae, is a large double-stranded DNA virus that infects shrimp and other crustaceans. By alignment of three completely sequenced isolates originating from Taiwan (WSSV-TW), China (WSSV-CN) and Thailand (WSSV-TH), the variable loci in the genome were mapped. The variation suggests the spread of WSSV from a common ancestor originating from either side of the Taiwan Strait to Thailand, but support for this hypothesis through analysis of geographical intermediates is sought. RFLP analysis of eight Vietnamese WSSV isolates, of which six were collected along the central coast (VN-central) and two along the south coast (VN-south), showed apparent sequence variation in the variable loci identified previously. These loci were characterized in detail by PCR amplification, cloning and sequencing. Relative to WSSV-TW, all VN-central isolates showed a similar to8.5 kb deletion in the major variable region ORF23/24, whereas the VN-south isolates contain a deletion of similar to11(.)5 or similar to12(.)2 kb, compared to a similar to1(.)2 or similar to13(.)2 kb deletion in WSSV-CN and WSSV-TH, respectively. The minor variable region ORF14/15 showed deletions of various sizes compared with WSSV-TH for all eight VN isolates. The data suggest that the VN isolates and WSSV-TH have a common lineage, which branched off from WSSV-TW and WSSV-CN early on, and that WSSV entered Vietnam by multiple introductions. A model is presented for the spread of WSSV from either side of the Taiwan Strait into Vietnam based on the gradually increasing deletions of both 'variable regions'. The number and order of repeat units within ORF75 and ORF125 appeared to be suitable markers to study regional spread of WSSV

Beyond Disentangled Representations: An Attentive Angular Distillation Approach to Large-scale Lightweight Age-Invariant Face Recognition

Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architecture for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee its robustness. This work presents a novel Attentive Angular Distillation (AAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, AAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, AAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our AAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach. This work also presents a new longitudinal face aging (LogiFace) database for further studies in age-related facial problems in future.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1062