Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.

Synthetic aperture radar (SAR) satellite has been widely applied in real-time flood monitoring as that they are not affected by extreme weather conditions. However, there is no automatic method to quickly and accurately extract flood areas with SAR satellite images. In this article, a UNet combined with the attention mechanism (UNet-CBAM) method has been proposed for extracting flood submerged areas, and both Longgan Lake and Dahuchi in Poyang Lake Basin are selected as the test sites. Based on the polarization characteristics of two Sentinel-1A data of Poyang Lake, both UNet and UNet-CBAM extraction methods are utilized to extract the flood areas, respectively. Compared with traditional SAR image water extraction methods, simulation results demonstrate that UNet can obtain more satisfactory results in recall, precision, and F 1 -parameter, but it has no capability to guarantee continuity in edges and small bodies of water. Moreover, our proposed UNet-CBAM method can further improve recall, precision, and F 1 -parameter, respectively. Specifically, when compared with UNet, its recall is increased by 0.8% and 1.2% while F 1 -parameter is improved by 0.6% and 0.8%, respectively, in the two test sites.

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