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A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism

by Liwen Zhang, Wenhao Wei, Bo Qiu, Ali Luo, Mingru Zhang and Xiaotong Li

Remote Sens. 2022, 14(16), 3970; https://doi.org/10.3390/rs14163970 - 16 Aug 2022

Cited by 11 | Viewed by 3266

Abstract

Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural network (CNN) named MA-SegCloud is proposed for segmenting cloud images based on a multibranch asymmetric convolution module (MACM) and an attention mechanism. The MACM is composed of asymmetric convolution, depth-separable convolution, and a squeeze-and-excitation module (SEM). The MACM not only enables the network to capture more contextual information in a larger area but can also adaptively adjust the feature channel weights. The attention mechanisms SEM and convolutional block attention module (CBAM) in the network can strengthen useful features for cloud image segmentation. As a result, MA-SegCloud achieves a 96.9% accuracy, 97.0% precision, 97.0% recall, 97.0% F-score, 3.1% error rate, and 94.0% mean intersection-over-union (MIoU) on the Singapore Whole-sky Nychthemeron Image Segmentation (SWINySEG) dataset. Extensive evaluations demonstrate that MA-SegCloud performs favorably against state-of-the-art cloud image segmentation methods. Full article

(This article belongs to the Special Issue Deep Learning-Based Cloud Detection for Remote Sensing Images)

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13 pages, 531 KB

Open AccessArticle

Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network

by Feng Liu, Xuan Zhou, Xuehu Yan, Yuliang Lu and Shudong Wang

Mathematics 2021, 9(2), 189; https://doi.org/10.3390/math9020189 - 19 Jan 2021

Cited by 22 | Viewed by 4423

Abstract

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance. Full article

(This article belongs to the Special Issue Mathematical Mitigation Techniques for Network and Cyber Security)

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