RESUMO
As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth's surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.
RESUMO
Animal hosts infected and killed by parasitoid fungi become nutrient-rich cadavers for saprophytes. Bacteria adapted to colonization of parasitoid fungi can be selected and can predominate in the cadavers, actions that consequently impact the fitness of the parasitoid fungi. In Taiwan, the zombie fungus, Ophiocordyceps unilateralis sensu lato (Clavicipitaceae: Hypocreales), was found to parasitize eight ant species, with preference for a principal host, Polyrhachis moesta. In this study, ant cadavers grew a fungal stroma that was predominated by Bacillus cereus/thuringiensis. The bacterial diversity in the principal ant host was found to be lower than the bacterial diversity in alternative hosts, a situation that might enhance the impact of B. cereus/thuringiensis on the sympatric fungus. The B. cereus/thuringiensis isolates from fungal stroma displayed higher resistance to a specific naphthoquinone (plumbagin) than sympatric bacteria from the environment. Naphthoquinones are known to be produced by O. unilateralis s. l., and hence the resistance displayed by B. cereus/thuringiensis isolates to these compounds suggests an advantage to B. cereus/thuringiensis to grow in the ant cadaver. Bacteria proliferating in the ant cadaver inevitably compete for resources with the fungus. However, the B. cereus/thuringiensis isolates displayed in vitro capabilities of hemolysis, production of hydrolytic enzymes, and antagonistic effects to co-cultured nematodes and entomopathogenic fungi. Thus, co-infection with B. cereus/thuringiensis offers potential benefits to the zombie fungus in killing the host under favorable conditions for reproduction, digesting the host tissue, and protecting the cadaver from being taken over by other consumers. With these potential benefits, the synergistic effect of B. cereus/thuringiensis on O. unilateralis infection is noteworthy given the competitive relationship of these two organisms sharing the same resource.