Coastal soil pollution detection and business English teaching index construction based on deep feature fusion

With the advent of the Internet age, network information technology is rapidly entering college English classes, which fundamentally changes the mode of college English teaching. In college English classroom teaching mode, as a brand teaching form, College English multimedia network teaching environment has brought advantages to classroom teaching, but also brought about impacts on teaching concepts, teaching models, teaching methods and other aspects. There are some phenomena that are inconsistent with the reform model at the students, teachers and the environment. The balance of traditional English classroom teaching has been broken, which has affected the smooth progress of college business English classroom teaching mode reform. It is very important to analyze and resolve these imbalances and find ecological methods for optimizing university English education. In this context, the advent of multimedia-assisted education technology has provided better conditions for the implementation of Business English classroom education in universities. Multimedia-powered business English education allows teachers to create a better language learning environment in class more conveniently and quickly, helping students acquire grammar knowledge and achieve their educational objectives.

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LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

IEEE Access ◽

10.1109/access.2021.3058305 ◽

2021 ◽

Vol 9 ◽

pp. 26138-26146

Author(s):

Xue Ni ◽

Huali Wang ◽

Fan Meng ◽

Jing Hu ◽

Changkai Tong

Keyword(s):

Feature Fusion ◽

Deep Feature

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High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

Remote Sensing ◽

10.3390/rs13020328 ◽

2021 ◽

Vol 13 (2) ◽

pp. 328

Author(s):

Wenkai Liang ◽

Yan Wu ◽

Ming Li ◽

Yice Cao ◽

Xin Hu

Keyword(s):

High Resolution ◽

Image Classification ◽

Feature Fusion ◽

Representation Learning ◽

Sar Image ◽

Gabor Filtering ◽

Feature Maps ◽

Sar Images ◽

Multi Scale ◽

Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

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