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Spectral unmixing is a major technique for the further development of hyperspectral analysis. It aims to determine the corresponding proportion (fractional abundance) of the basic spectral signatures (endmembers) blindly at the subpixel level. Recently, the learning-based method has received much attention in hyperspectral unmixing, and autoencoders have been effectively designed to solve the unsupervised scenarios of unmixing. However, their ability to extract physically meaningful endmembers remains limited, and the performance has not been satisfactory. In this article, we propose a novel two-stream network, termed TANet, to address the above problems. The network consists of a two-stream architecture. First, superpixel segmentation is adopted as preprocessing to extract the endmember bundles from the image. Then, the first stream learns a mapping from the pseudopure pixels to their corresponding abundances. The second stream is conducting the same untied-weighted autoencoder to minimize reconstruction errors from the original pixel data. By learning from the pure or nearly pure candidate pixels to correct the weights of unmixing, the proposed TANet exhibits a more accurate and interpretable unmixing performance. Extensive experiments on both synthetic and real hyperspectral data demonstrate that the proposed TANet can outperform the other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In-service bridge structural performance analysis and prediction are usually complicated and challenging because of many unknown and uncertain factors. Contrary to the traditional structural appearance inspections and load tests, structural health monitoring (SHM) can provide a perspective for online analysis, prediction, and early warning. So far, SHM has been widely used in many bridge structures, and a lot of bridge SHM data have also been collected. However, the existing studies usually focus on some independent and unsystematic analysis methods, which are hard to use widely in engineering applications to reveal the overall structural performance. This study focuses on the structural performance analysis and prediction of the highway in-service bridge. The dynamic problems in bridge SHM are pointed out firstly, followed by a detailed analysis about the characteristics of bridge SHM data. With the consideration of different characteristics, three targeted analysis methods are proposed. An urban concrete-filled steel tube (CFST) truss girder bridge (opened to traffic in 1995) is also presented, which once experienced some prominent vibration problems. The bridge SHM system is designed and stalled after several appearance inspections, load tests, and some reinforcement measures. The data mining methods proposed (distribution function, association analysis, and time-series analysis) are employed for the analysis and prediction of structural response and deterioration extent. This study can provide some references for maintenance and management and can also build a foundation for further online analysis and early warning. [ABSTRACT FROM AUTHOR]