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MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

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International Journal of Digital Earth 2023年第1期16卷 1-30页

作者： Zhihui Tian Xiaoyu Guo Xiaohui He Panle Li Xijjie Cheng Guangsheng Zhou School of Geoscience and Technology Zhengzhou UniversityZhengzhouPeople's Republic of China Ecometeorology Joint Laboratory of Zhengzhou University and Chinese Academy of Meteorological Science ZhengzhouPeople's Republic of China School of Information Engineering Zhengzhou UniversityZhengzhouPeople's Republic of China

Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water ***,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of ***,it is a great challenge to completely and effectively extract plateau *** this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake *** consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous *** MPPM module aggregated the contextual information of various lakes *** applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological ***,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

关键词： Remote sensing imagery The Qinghai-Tibet Plateau lake extraction deep learning multiscale feature context information aggregation

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feature Fusion Multi_XMNet Convolution Neural Network for Clothing Image Classification

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Journal of Donghua University(English Edition) 2021年第6期38卷 519-526页

作者： ZHOU Honglei PENG Zhifei TAO Ran ZHANG Lu College of Fashion and Design Donghua University Shanghai 200051 China School of Computer Science and Technology Donghua University Shanghai 201620 China

Faced with the massive amount of online shopping clothing images,how to classify them quickly and accurately is a challenging task in image *** this paper,we propose a novel method,named Multi_XMNet,to solve the clothing images classification *** proposed method mainly consists of two convolution neural network(CNN)*** branch extracts multiscale features from the whole expressional image by Multi_X which is designed by improving the Xception network,while the other extracts attention mechanism features from the whole expressional image by MobileNetV3-small *** multiscale and attention mechanism features are aggregated before making ***,in the training stage,global average pooling(GAP),convolutional layers,and softmax classifiers are used instead of the fully connected layer to classify the final features,which speed up model training and alleviate the problem of overfitting caused by too many *** comparisons are made in the public DeepFashion *** experimental results show that the classification accuracy of this method is 95.38%,which is better than InceptionV3,Xception and InceptionV3_Xception by 5.58%,3.32%,and 2.22%,*** proposed Multi_XMNet image classification model can help enterprises and researchers in the field of clothing e-commerce to automaticly,efficiently and accurately classify massive clothing images.

关键词： feature extraction feature fusion multiscale feature convolution neural network(CNN) clothing image classification

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Multidimensional attention and multiscale upsampling for semantic segmentation

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Journal of Measurement Science and Instrumentation 2022年第1期13卷 68-78页

作者： LU Zhongda ZHANG Chunda WANG Lijing XU Fengxia School of Mechanical and Electrical Engineering Qiqihar University Qiqihar 161000 China Heilongjiang Province Collaborative Innovation Center for Intelligent Manufacturing Equipment Industrialization Qiqihar 161000 China

Semantic segmentation is for pixel-level classification tasks,and contextual information has an important impact on the performance of *** order to capture richer contextual information,we adopt ResNet as the backbone network and designs an encoder-decoder architecture based on multidimensional attention(MDA)module and multiscale upsampling(MSU)*** MDA module calculates the attention matrices of the three dimensions to capture the dependency of each position,and adaptively captures the image *** MSU module adopts parallel branches to capture the multiscale features of the images,and multiscale feature aggregation can enhance contextual information.A series of experiments demonstrate the validity of the model on Cityscapes and Camvid datasets.

关键词： semantic segmentation attention mechanism multiscale feature convolutional neural network(CNN) residual network(ResNet)

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A New Approach to Edge Extraction by Image Decomposition and Restoration

A New Approach to Edge Extraction by Image Decomposition and...

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2000年第16届世界计算机大会

作者： Jun Miao Wen Gao Chunling Ma Yiqiang Chen Xiaoyan Sun Digital Media Lab Institute of Computing TechnologyChinese Academy of Sciences Department of Computer Science Beijing Polytechnic University

＜正＞This paper suggests a new approach to edge extraction in terms of image decomposition and restoration,which adopts some specific edge extraction operator as seed operator to extract edge in a group of sub-images produced by sub-sampling original image and restoring the extracted edge-pixels to their corresponding positions in original *** is a fast method to extract edge features at any specified scale,whose time complexity,excluding additional spot removal procedure, is equal to that of the seed edge operator conducted in the original image.

关键词： multiscale feature edge extraction image decomposition-restoration

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