Advancing Land Cover Mapping in Remote Sensing with Deep Learning

Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework

BiFeO3-Based Nanoceramics Prepared by Spark Plasma Sintering

AbstractBiFeO3-based nanopowders were prepared via a sol-gel method, in which the gel (with metal-nitrate, maleic acid and water used as raw materials) was sintered at 650°C for 2 hours. The aggregation of nanopowders was destroyed by high energy ball milling for 12 hours. BiFeO3-based nanoceramics were prepared by spark plasma sintering method. XRD results indicate that there are two phases, ZrO2 and BiFeO3, in the ceramics. The results of SEM observation show that the ceramic grain size is about 50nm in diameter. These phenomena and the changes of sintering parameters indicate that ZrO2 phase exits in the grain boundaries and inhabits the growth of BiFeO3 grains. The dielectric constant of nanoceramics, about 70, is stable between 102 Hz and106 Hz

Rapid, simple, and sensitive detection of the ompB gene of spotted fever group rickettsiae by loop-mediated isothermal amplification

BACKGROUND: Spotted fever caused spotted fever group rickettsiae (SFGR) is prevalent throughout China. In this study, we describe a rapid, simple, and sensitive loop-mediated isothermal amplification (LAMP) assay targeting the ompB gene of spotted fever group rickettsiae ideal for application in China. The LAMP assay has the potential to detect spotted fever group rickettsiae early in infection and could therefore serve as an alternative to existing methods. METHODS: A set of universal primers which are specific 7 common species of spotted fever group rickettsiae in China were designed using PrimerExplorer V4 software based on conserved sequences of ompB gene. The sensitivity, specificity and reproducibility of the LAMP were evaluated. The LAMP assay for detecting SFGR was compared with conventional PCR assays for sensitivity and specificity in early phase blood samples obtained from 11 infected human subjects. RESULTS: The sensitivity of the LAMP assay was five copies per reaction (25 μL total volume), and the assay did not detect false-positive amplification across 42 strains of 27 members of the order Rickettsiales and 17 common clinical pathogens. The LAMP assay was negative to typhus group rickettsiae including R. prowazekii and R. typhi for no available conserved sequences of ompB was obtained for designing primers. To evaluate the clinical applicability of the LAMP assay, a total of 11 clinical samples, 10 samples confirmed serologically (3 cases), ecologically (1 case), by real-time polymerase chain reaction (PCR; 2 cases), ecologically and by real-time PCR (1 case), and serologically and by real-time PCR (3 cases) were analyzed by the ompB LAMP assay. Data were validated using a previously established nested PCR protocol and real-time PCR. A positive LAMP result was obtained for 8 of the 10 confirmed cases (sensitivity, 73%; specificity, 100%), while none of these samples were positive by nested PCR (sensitivity, 0%; specificity, 100%). CONCLUSIONS: The LAMP assay described here is the most reliable among the three methods tested and would be an ideal choice for development as a rapid and cost-effective means of detecting SFGR in China