Most of the existing synthetic aperture radar(SAR) automatic target recognition(ATR) algorithm are based on data-driven. However, there is not enough data for some specific target recognition to be trained. In this paper, a siamese network with parameter sharing is created, and then the simulated and real SAR images of the available targets are used as sample pair inputs, and labeled as positive and negative sample pairs based on whether the input sample pairs are of the same category, so that the network can be trained to extract domain invariant features, and then use classifiers to achieve the recognition task of non-homologous targets. The proposed method is validated on the moving and stationary target acquisition and recognition (MSTAR) dataset, The results are that the accuracy of on trained by ten pairs simulation and real SAR images are higher 93.92%, and the accuracy trained by on only one reaches 81.17%.

Knowledge graph currently has two main data models: RDF graph and property graph. The query language on RDF graph is SPARQL, while the query language on property graph is mainly Cypher. Different data models and query languages hinder the wider application of knowledge graphs. In this demonstration, we propose a unified interoperable knowledge graph database system, UniKG. (1) Based on the relational model, a unified storage scheme is utilized to efficiently store RDF graphs and property graphs, and support the query requirements of knowledge graphs. (2) Using the characteristicset-based method, the storage problem of untyped entities is addressed in UniKG. (3) UniKG realizes the interoperability of SPARQL and Cypher, and enables them to interchangeably operate on the same knowledge graph. (4) With a unified Web interface, users are allowed to query with two different languages over the same knowledge graph and visualize query results and explanations.

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral differences between different materials, but in fact, it is difficult to distinguish between background and anomalies because the samples of anomalous pixels in HSIs are limited and susceptible to background and noise. To explore the discriminant features, a spectral adversarial feature learning (SAFL) architecture is specially designed for hyperspectral anomaly detection in this article. In addition to reconstruction loss, SAFL also introduces spectral constraint loss and adversarial loss in the network with batch normalization to extract the intrinsic spectral features in deep latent space. To further reduce the false alarm rate, we present an iterative optimization approach by a weighted suppression function that depends on the contribution rate of each feature to the detection. In particular, the structure tensor matrix is adopted to adaptively calculate the contribution rate of each feature. Benefiting from these improvements, the proposed method is superior to the typical and state-of-the-art methods either in detection probability or false alarm rate.

Reliable detection of anomalies without any prior information is a critical yet challenging task in many applications, not least military and civilian fields. An intelligent anomaly detection system would use the material-specific spectral information in hyperspectral images (HSIs), thereby avoiding the loss of visually confusing objects. However, conventional hyperspectral anomaly detection methods are mainly achieved in an unsupervised way leading to limited performance due to lack of prior knowledge. In this article, we propose a novel autoencoder and adversarial-learning based semisupervised background estimation model (SBEM) that is trained only on the background spectral samples in order to accurately learn the background distribution. In particular, an unsupervised background searching method is firstly conducted on the original HSIs to search the background spectral samples. Our proposed SBEM consists of an encoder, a decoder, and a discriminator to thoroughly capture background distribution. Furthermore, jointly minimizing the reconstruction loss, spectral loss, and adversarial loss during training aids the model to learn the background distribution as required. Experiments on four real HSIs demonstrate that compared to the current state-of-the-art, the proposed framework yields higher detection capability and lower false alarm rate, which shows that it has a significant benefit in the tradeoff between detection accuracy and false alarm rate.

Accurate precipitation data with high spatial resolution is important for hydrological, meteorological, and environmental applications. Tropical Rainfall Measuring Mission (TRMM) provides multiple precipitation products with a spatial resolution of 0.25°, and the resolution is too coarse to meet the requirements for hydrological study on local regions and watersheds scale. In this paper, geographically weighted regression (GWR) and multiple linear regression models were used for TRMM data downscaling. GWR is suitable to explore and describe the complex relationship between precipitation and other environmental variables. Global regression analysis applied uniform model parameters over space, which may provide a poor description of the local relationship and could result in important details being missed. By adopting GWR, the strength of the relationship among TRMM, NDVI, and DEM increased markedly, with a minimum of 91% of the variation in TRMM values explained by those in NDVI and DEM (R2= 0.911). Comparing the downscaled precipitation with the observed precipitation data, the results show that GWR has an increased R2of 0.758 and reduced RMSE of 68.4mm, comparing with the global regression method with R2of 0.601 and RMSE of 88.4mm.