Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a proper land-cover label. Recently, convolutional neural networks (CNNs) have shown superior performance. To identify the land-cover label, CNN-based methods exploit the adjacent pixels as an input HSI cube, which simultaneously contains spectral signatures and spatial information. However, at the edge of each land-cover area, an HSI cube often contains several pixels whose land-cover labels are different from that of the center pixel. These pixels, named interfering pixels, will weaken the discrimination of spectral-spatial features and reduce classification accuracy. In this article, a spectral-spatial attention network (SSAN) is proposed to capture discriminative spectral-spatial features from attention areas of HSI cubes. First, a simple spectral-spatial network (SSN) is built to extract spectral-spatial features from HSI cubes. The SSN is composed of a spectral module and a spatial module. Each module consists of only a few 3-D convolution and activation operations, which make the proposed method easy to converge with a small number of training samples. Second, an attention module is introduced to suppress the effects of interfering pixels. The attention module is embedded into the SSN to obtain the SSAN. The experiments on several public HSI databases demonstrate that the proposed SSAN outperforms several state-of-the-art methods.

As an indispensable task for traffic management department, road maintenance has attracted much attention during the last decade due to the rapid development of traffic network. As is known, crack is the early form of many road damages, and repair it in time can significantly save the maintenance cost. In this case, how to detect crack regions quickly and accurately becomes a huge demand. Actually, many image processing technique based methods have been proposed for crack detection, but their performances can not meet our expectations. The reason is that, most of these methods use bottom features such as color and texture to detect the cracks, which are easily influenced by the varied conditions such as light and shadow. Inspired by the great successes of machine learning and artificial intelligence, this paper presents a sample and structure guided network for detecting road cracks. Specifically, the proposed network is based on U-Net architecture, which remains the details from input to output by using skip connection strategy. Then, because the scale of crack samples is much smaller than that of non-crack ones, directly using the conventional cross entropy loss can not optimize the network effectively. In this case, the Focal loss is utilized to address the model optimization problem. Additionally, we incorporate the self-attention strategy into the proposed network, which enhances its stability by encoding the 2-order information among different local regions into the final features. Finally, we test the proposed method on four datasets, three public ones with labels and a photographed one without labels, to validate its effectiveness. It is noteworthy that, for the photographed dataset, we design a series of image processing strategies such as contrast enhancement to improve the generalization capability of the proposed method.

As a traditional Chinese herbal formula, Xiasangju (XSJ) is widely used in China for antipyresis and influenza treatment. However, XSJ still fails to have a comprehensive summary of the research progress in the last decade. This review summarizes the advanced research on the extraction process, phytochemistry, pharmacological activity, and quality control of XSJ. Current research mainly focuses on quality control and the pharmacological effects of single herbs and active ingredients, but many pharmacological mechanisms of the formula are unclear. The development of active ingredients reflects the active characteristics of triterpenes, phenolic acids and flavonoids, but the hepatotoxicity ofPrunella vulgarisL. has not been taken into account. XSJ has extensive historical practical experiences, while systematic clinical trials remain lacking. Therefore, it is necessary to study the active ingredients and define the mechanisms of XSJ to develop multiple applications, and further studies on the dose range between its hepatoprotective activity and hepatotoxicity are necessary to improve the safety of the clinical application. In this review, the current problems are discussed to facilitate the reference basis for the subsequent research on the development of XSJ and future application directions.