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A Hybrid Inversion Method Based on SDM and ANNs Considering Electromagnetic Response Laws

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Lang, Shinan Huang, Wenbin Huang, Ling Liu, Xiaojun Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Elect Key Lab Electromagnet Radiat & Sensing Technol Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst AIRCAS Beijing 100094 Peoples R China

Among the inversion methods for airborne transient electromagnetic (ATEM) data, the hybrid inversion method integrates the iterative optimization framework with artificial neural networks (ANNs), ensuring inversion accuracy while enhancing the generalization capability of neural networks. However, this method faces challenges in terms of slow computation speeds due to its lower updated step length and the lack of consideration for electromagnetic response laws. Our method adopts a supervised descent method (SDM) framework to supervise the ANNs, obtaining a longer updated step length. On the basis of the SDM framework, we have considered the electromagnetic response laws and designed RNN-ResNet and 1-D-UNet networks to update the conductivity model, improving the computing speed. Through numerical ablation experiments, we validated the effectiveness of our proposed method and compared the inversion results with those obtained using the traditional hybrid method. Additionally, we conducted tests on bundle fringe distribution, inversion fitting loss, noise sensitivity, and inversion speed for both methods using measured data to evaluate their performance in practical applications. The experimental findings demonstrate that our method achieves the same level of inversion accuracy and generalization ability as the traditional hybrid method while enhancing inversion speed by up to 68.5%.

关键词： Conductivity Electromagnetics Accuracy Iterative methods Optimization Data models Atmospheric modeling Airborne transient electromagnetic (ATEM) data inversion controlled-source electromagnetics (CSEM) encoder-decoder structure rapid inversion method supervised descent method (SDM)

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Rethinking Transformers for Semantic Segmentation of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Liu, Yuheng Zhang, Yifan Wang, Ye Mei, Shaohui Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Transformer has been widely applied in image processing tasks as a substitute for convolutional neural networks (CNNs) for feature extraction due to its superiority in global context modeling and flexibility in model generalization. However, the existing transformer-based methods for semantic segmentation of remote sensing (RS) images are still with several limitations, which can be summarized into two main aspects: 1) the transformer encoder is generally combined with CNN-based decoder, leading to inconsistency in feature representations;and 2) the strategies for global and local context information utilization are not sufficiently effective. Therefore, in this article, a global-local transformer segmentor (GLOTS) framework is proposed for the semantic segmentation of RS images to acquire consistent feature representations by adopting transformers for both encoding and decoding, in which a masked image modeling (MIM) pretrained transformer encoder is adopted to learn semantic-rich representations of input images and a multiscale global-local transformer decoder is designed to fully exploit the global and local features. Specifically, the transformer decoder uses a feature separation-aggregation module (FSAM) to utilize the feature adequately at different scales and adopts a global-local attention module (GLAM) containing global attention block (GAB) and local attention block (LAB) to capture the global and local context information, respectively. Furthermore, a learnable progressive upsampling strategy (LPUS) is proposed to restore the resolution progressively, which can flexibly recover the fine-grained details in the upsampling process. The experiment results on the three benchmark RS datasets demonstrate that the proposed GLOTS is capable of achieving better performance with some state-of-the-art methods, and the superiority of the proposed framework is also verified by ablation studies. The code will be available at https://***/lyhnsn/GLOTS.

关键词： encoder-decoder structure global-local transformer remote sensing (RS) semantic segmentation

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Depth image super-resolution using correlation-controlled color guidance and multi-scale symmetric network

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PATTERN RECOGNITION 2020年 107卷 107513-107513页

作者： Li, Tao Lin, Hongwei Dong, Xiucheng Zhang, Xiaohua Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China Northwest Minzu Univ Coll Elect Engn Lanzhou 730000 Peoples R China Hiroshima Inst Technol Dept Informat Syst & Management Hiroshima 7315193 Japan

Depth image super-resolution (DISR) is an effective solution to improve the quality of depth images captured by real world low-cost cameras. In this paper, we propose a multi-scale symmetric network with the correlation-controlled color guidance block (CCGB) for DISR. The proposed network consists of two multi-scale sub-networks to respectively provide guidance and estimate depth. A symmetric unit (SU), which is a mini-encoder-decoder structure with residual learning, is designed and used as a basic network atom. The encoder part in SU aims to extract essential features, while the decoder part works to restore edge details. The way the SU processes information matches well with the textureless and sharpedge characteristics of depth images. The two sub-networks present a high-level symmetric structure connected by dense guidance links in between. Based on the correlation analyses between the two subnetworks, each guidance link will transfer information trough a CCGB designed to implement channel-wise re-weighting mechanism. The accurate color guidance from CCGB helps avoiding artifacts introduced by non-co-occurrence of depth discontinuities and color edges. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art DISR works. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Depth image super-resolution Deep convolutional neural network encoder-decoder structure Color guidance Channel correlation

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Strip Attention Networks for Road Extraction

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REMOTE SENSING 2022年第18期14卷 4516-4516页

作者： Huan, Hai Sheng, Yu Zhang, Yi Liu, Yuan Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Nanjing Nanji Intelligent Agr Machinery Technol R Nanjing 210008 Peoples R China

In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding roads in remote sensing images, and the existence of shadows and occlusion. To deal with this problem, this paper proposes strip attention networks (SANet) for extracting roads in remote sensing images. Firstly, a strip attention module (SAM) is designed to extract the contextual information and spatial position information of the roads. Secondly, a channel attention fusion module (CAF) is designed to fuse low-level features and high-level features. The network is trained and tested using the CITY-OSM dataset, DeepGlobe road extraction dataset, and CHN6-CUG dataset. The test results indicate that SANet exhibits excellent road segmentation performance and can better solve the problem of poor road segmentation continuity compared with other networks.

关键词： remote sensing image road extraction attention module encoder-decoder structure

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DAEA-Net: Dual Attention and Elevation-Aware Networks for Airborne LiDAR Point Cloud Semantic Segmentation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhu, Yurong Liu, Zhihui Liu, Changhong China Univ Geosci Sch Math & Phys Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China

Semantic segmentation of airborne laser scanning (ALS) point clouds remains a challenging task due to the complexity and diversity of 3-D scenes in the real world. Currently, most deep learning-based airborne LiDAR point cloud segmentation methods prioritize designing local feature extraction operators while overlooking the long-range dependencies among neighborhoods and the inherently diverse properties of point cloud data. To address these issues, this article introduces a dual-attention and elevation-aware airborne LiDAR point cloud semantic segmentation network (DAEA-Net) built upon an encoding-decoding architecture. First, we develop a cross multiple anti-affine attention (CMAAA) module that effectively captures global contextual information across different neighborhoods through interactive learning of multiple features. Second, we introduce an elevation awareness (EA) module that uses normal vectors to establish a geometric similarity discriminant for each neighboring point. It incorporates an autoencoder architecture to fuse elevation information, enhancing the horizontal structural dissimilarity between objects of similar height while enriching the representation of elevation data. Additionally, to compensate for the potential information loss in the encoding-decoding hierarchical structure, we design a lightweight U-global attention (UGA) module to link decoding and encoding hierarchical levels. It merges features of different resolutions and levels during downsampling and upsampling through pooling while utilizing the self-attention mechanism to enhance the network's global expression capability. The proposed DAEA-Net enhances ALS semantic segmentation performance by enabling interactive learning of multiple features and effectively representing elevation information. Extensive experiments conducted on two datasets demonstrate that our method delivers superior semantic segmentation performance compared to several existing advanced techniques.

关键词： Point cloud compression Feature extraction Semantic segmentation Three-dimensional displays Laser radar Geology Encoding Attention mechanism elevation information encoder-decoder structure global perception point cloud semantic segmentation

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Cascaded transformer U-net for image restoration

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SIGNAL PROCESSING 2023年第1期206卷

作者： Yan, Longbin Zhao, Min Liu, Shumin Shi, Shuaikai Chen, Jie Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Blueye Intelligence Zhenjiang Peoples R China

Image restoration is one of the most important computer vision tasks, aiming at recovering high-quality images from degraded or low-quality observations. The restoration methods based on convolutional neural networks (CNNs) have achieved attractive performance, however, as convolutions only intake local information, CNN-based methods have limitations in modeling objects in long ranges and extracting global information. In addition, existing one-stage methods damage the performance due to lacking diversified receptive fields. In this paper, we propose a multi-stage cascaded transformer architecture for image restoration. Firstly, the Swin transformer based encoder relying on self-attention is used to improve the modeling ability for long-range objects and outputs hierarchical multi-level semantic features. Then, a shape perceiving module is designed and embedded in the decoder to enhance the representation of irregular objects, Moreover, a multi-stage cascaded encoder-decoder architecture possessing diversified receptive fields is proposed to progressively obtain fine restoration results and thus boost the performance. We conduct extensive experiments, including image deraining, underwater image enhancement, near infrared image colorization and low-light image enhancement. The results show that our proposed method can achieve comparable or better performance than state-of-the-art methods while with less training and inference costs. (c) 2022 Published by Elsevier B.V.

关键词： Image deraining Underwater image enhancement Near infrared image colorization encoder-decoder structure Long -range dependence modeling

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LIGHTWEIGHT MESH CRACK DETECTION ALGORITHM BASED ON EFFICIENT ATTENTION MECHANISM

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2023年第3期38卷 170-179页

作者： Hang, Die Yang, Jianxi Jiang, Shixin Li, Hao Zou, Xiaoxue Tang, Chuncheng Liu, Die Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing Peoples R China Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing Peoples R China Chongqing Jiaotong Univ Sch Civil Engn Chongqing Peoples R China

Cracks are one of the most common anomalies in concrete structures, affecting their safety, and thus have received much attention. However, most of the previous studies have focused on regular cracks, while fewer studies have analysed mesh cracks. Due to the characteristics of early appearance and high complexity, mesh cracks cause severe damage to concrete structures. Therefore, the automatic detection of mesh cracks is crucial to the safety of concrete structures. As mesh cracks consist of many fine branches, which can cause discontinuous results, this paper proposes a lightweight mesh crack detection model (MCM-Net) based on an efficient attention mechanism. The proposed network adopts an encoder-decoder structure and introduces improved efficient channel attention that assigns high weights to crack pixels. The introduction of lightweight convolutional modules into the proposed network reduces the computational cost, while the superposition of max -pooling and mean-pooling enables the extraction of more minutiae pixels. The proposed network is verified by experiments on the crack-detection (CD) and bridge-crack-image (BCI) datasets. The experimental results show that the proposed network can improve the stability and computational efficiency of mesh crack detection.

关键词： Mesh crack detection encoder-decoder structure lightweight convolutional module efficient channel attention max-pooling mean-pooling

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BFBE-Net: deep bilateral fusion and bilateral embedded network for real-time semantic segmentation

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JOURNAL OF ELECTRONIC IMAGING 2023年第4期32卷 043031-043031页

作者： Hou, Zhiqiang Cheng, Minjie Dai, Nan Ma, Sugang Yang, Xiaobao Fan, Jiulun Xian Univ Posts & Telecommun Inst Comp Xian Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian Peoples R China

Real-time semantic segmentation has been challenging, and the fusion of features from different branches remains crucial to improvement. The two-branch structure has shown promising results in real-time semantic segmentation. However, upsampling feature maps from the semantic branch to match the detail branch leads to a loss of object feature information and compromises segmentation accuracy. We propose a deep bilateral fusion and bilateral embedded network (BFBE-Net) based on the encoder-decoder structure for real-time semantic segmentation to address these issues. The BFBE-Net adopts a two-branch design in the encoder, with a top-down fusion module and a bottom-up fusion module designed to integrate multi-scale context information in the channel dimension, and assigns different weights to detailed information and semantic information to enhance information characteristics. In the decoder, a bilateral embedded attention module under the guidance of spatial and channel attention integrates semantic and spatial features, gradually upsampling feature maps to reduce the loss of feature information. In addition, an enhanced aggregation pyramid pooling module is designed to efficiently extract contextual information by combining depth-wise asymmetric convolution. The proposed algorithm is evaluated on two benchmark datasets, Cityscapes and CamVid, achieving 78.5% mean intersection over union (mIoU) at 82 frames per second (FPS) on the Cityscapes test set and 79.2% mIoU at 131 FPS on the CamVid test set. The proposed BFBE-Net not only improves segmentation accuracy but also ensures real-time performance.

关键词： real-time semantic segmentation convolutional neural network encoder-decoder structure two-branch structure feature fusion

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An improved algorithm for semantic segmentation of remote sensing images based on DeepLabv3+ 19

An improved algorithm for semantic segmentation of remote se...

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Proceedings of the 5th International Conference on Communication and Information Processing

作者： Jiaqi Liu Zhili Wang Kangxin Cheng Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450372589

Remote sensing image segmentation is a more and more popular topic currently, and obviously it depends on the improvement of semantic segmentation. encoder-decoder structure is an effective architecture in semantic segmentation. Since the encoder network helps to obtain various scale feature from the deep convolution layers, and the decoder network often help to recover the spatial resolution and location information in detail. Drawing on this idea, Google proposed the DeepLabv3+ [9] after DeepLabv3[8], which reused DeepLabv3 as its encoder, and added the decoder part to help the network to recover accurate location information. However, the decoder part of DeepLabv3+ is simple and sometimes it's difficult to obtain enough details from the encoder, and achieves not so good results on remote sensing images. Therefore, we design our decoder by adding more skip connections and convolution layers, which improves the result of building detection in the dataset SpaceNet [1].

关键词： encoder-decoder structure remote sensing image semantic segmentation

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