Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

An Early Cretaceous Tribosphenic Mammal and Metatherian Evolution

Abstract: Derived features of a new boreosphenidan mammal from the Lower Cretaceous Yixian Formation of China suggest that it has a closer relationship to metatherians (including extant marsupials) than to eutherians (including extant placentals). This fossil dates to 125 million years ago and extends the record of marsupial relatives with skeletal remains by 50 million years. It also has many foot structures known only from climbing and tree-living extant mammals, suggesting that early crown therians exploited diverse niches. New data from this fossil support the view that Asia was likely the center for the diversification of the earliest metatherians and eutherians during the Early Cretaceous.

Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China

Abstract: US NASA [NNX09AE17G, NNX09AV56G]; NOAA [NA06NES4400004]; National Natural Science Foundation of China [40871168]; Ministry of Education of China [B07034]

Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features

Abstract: Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest.