We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF.

We present a spatial–spectral decoupling interaction network for multispectral imagery change detection, which can exploit the underlying information of the multispectral imagery adequately through simultaneously considering the discriminative attribute of each pixel and robust spatial structure of the corresponding patch. Specifically, a 1-D convolutional neural network (1D-CNN) is applied to the spectral vector of each pixel to extract its discriminative feature, while a 2D-CNN is applied to the patch centering on the corresponding pixel to explore the spatial structure information. In addition, an interaction mechanism is incorporated into the feature fusion module to enhance the spatial–spectral consistency.

For large industrial applications, system test cases are still often described in natural language (NL), and their number can reach thousands. Test automation is to automatically execute the test cases. Achieving test automation typically requires substantial manual effort for creating executable test scripts from these NL test cases. In particular, given that each NL test case consists of a sequence of NL test steps, testers first implement a test API method for each test step and then write a test script for invoking these test API methods sequentially for test automation. Across different test cases, multiple test steps can share semantic similarities, supposedly mapped to the same API method. However, due to numerous test steps in various NL forms under manual inspection, testers may not realize those semantically similar test steps and thus waste effort to implement duplicate test API methods for them. To address this issue, in this paper, we propose a new approach based on natural language processing to cluster similar NL test steps together such that the test steps in each cluster can be mapped to the same test API method. Our approach includes domain-specific word embedding training along with measurement based on Relaxed Word Mover’sDistance to analyze the similarity of test steps. Our approach also includes a technique to combine hierarchical agglomerative clustering and K-means clustering post-refinement to derive high-quality and manually-adjustable clustering results. The evaluation results of our approach on a large industrial mobile app, WeChat, show that our approach can cluster the test steps with high accuracy, substantially reducing the number of clusters and thus reducing the downstream manual effort. In particular, compared with the baseline approach, our approach achieves 79.8% improvement on cluster quality, reducing 65.9% number of clusters, i.e., the number of test API methods to be implemented.