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Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning

SENSORS 2021年第9期21卷 3152-3152页

作者： Liang, Peng Shi, Wenzhong Ding, Yixing Liu, Zhiqiang Shang, Haolv Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Piesat Informat Technol Co Ltd Beijing 100195 Peoples R China

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning.

关键词： road extraction vector field learning high resolution remote sensing image encoder-decoder DCNN

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Using various pre-trained models for audio feature extraction in automated audio captioning

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Won, Hyejin Kim, Baekseung Kwak, Il-Youp Lim, Changwon Chung Ang Univ Dept Appl Stat Seoul 06974 South Korea Chung Ang Univ Inst Community Care & Hlth Equ Seoul 06974 South Korea

The DCASE automated audio captioning challenge aimed to construct a model that generates captions describing given audio. Our team developed a CNN14 encoder (pre-trained on AudioSet data) along with a Transformer decoder model that ranked sixth place in the competition. Many teams utilized pre-trained networks, and it was evident that more research into their utilization was required. This paper presented comprehensive experiments conducted with various encoder networks for the proposed system, including CNN10, CNN14 ResNet54, AST, VGGNet, and EfficientNet. The pre-trained networks of CNN10, CNN14, ResNet54, and AST were trained on AudioSet data, while the pre-trained networks of AST, VGGNet, and EfficientNet were trained on ImageNet data. The best outcomes were achieved when the pre-trained CNN10, trained on AudioSet data, was utilized as an encoder with the Transformer serving as a decoder, and fine-tuning applied. Moreover, a qualitative study confirmed that our model generates plausible captions for different types of audio.

关键词： Audio captioning Acoustic scene detection Transfer learning encoder-decoder Convolutional neural network Transformer

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Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks

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GEOPHYSICS 2021年第2期86卷 V131-V142页

作者： Liu, Qun Fu, Lihua Zhang, Meng China Univ Geosci Wuhan Sch Math & Phys Wuhan 430074 Peoples R China Cent China Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China

The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing. Deep learning (DL) has emerged as a popular tool for seismic interpolation;it learns priors from training data sets of incomplete/complete data pairs. However, these DL methods are restricted to training data because they are supervised. When the features of the testing and training data are different, the recovery performance decreases, which prevents practical application. We have introduced a "deep-seismic-prior-based" approach via a convolution neural network (CNN), which captures priors based on the particular structure of the CNN, but it does not need any training data set. The ill-posed inverse problem in seismic interpolation is thus solved using the CNN structure as a prior, and the learned network weights are the parameters that represent the seismic data. Because the convolutional filter weights are shared to achieve spatial invariance, the CNN structure can function as a regularizer to guide network learning. In our method, corrupted seismic data are reconstructed during the iterative process by minimizing the mean square error between the network output and the original data. We applied our method for interpolating irregularly and regularlymissing traces in prestack and poststack seismic data. The experimental results indicate that our approach outperforms the traditional singular spectrum analysis and the dealiased Cadzow methods commonly used in the reconstruction of such data.

关键词： convolutional neural networks deep seismic prior encoder-decoder seismic data reconstruction

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Hybrid Attention Distribution and Factorized Embedding Matrix in Image Captioning

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IEEE ACCESS 2020年 8卷 154453-154460页

作者： Wang, Jian Feng, Jie Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

Self-attention mechanisms are widely used in current encoder-decoder frameworks of image-captioning tasks. These mechanisms use the transformer module as the basic unit to compute the similarity between query and key vectors, and normalize them to obtain the attention distribution matrix, weighting and reconstructing the rich semantic query vectors. However, a single attention distribution matrix cannot express more complex intrinsic relations between query and key vectors. Some very important intrinsic relations can thus become lost. We therefore propose a hybrid attention distribution (HAD) that allows multiple distributions to be reconstructed to express deeper internal relations and avoids a single shallow attention distribution. In addition, to reduce the number of model parameters, we factorize the word-embedding matrix. This effectively increases the training efficiency and prevents the metrics from decreasing. We extensively evaluate our proposals using the MS-COCO image-captioning dataset. Our results outperform existing state-of-the-art methods on some metrics.

关键词： Self-attention image captioning transformer encoder-decoder

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TPET: Two-stage Perceptual Enhancement Transformer Network for Low-light Image Enhancement

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期116卷

作者： Cui, Hengshuai Li, Jinjiang Hua, Zhen Fan, Linwei Shandong Technol & Business Univ Future Intelligent Comp Coinnovat Ctr Shandong Coll & Univ Sch Comp Sci & Technol Yantai Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan Peoples R China

Low-light images captured under low light or backlight conditions can suffer from different types of degradation such as low visibility, strong noise and color distortion. In this paper, to solve the degradation problem of low -light images, we propose Two-stage Perceptual Enhancement Transformer Network(TPET) for Low-light Image Enhancement by combining the advantages of local spatial perception of convolutional neural network and global spatial perception of transformer. The method is generally divided into two stages: feature extraction stage and detail fusion stage. First, in the feature extraction stage, the encoder composed of transformers performs global feature extraction and expands the receptive field. Since the transformer lacks the ability to capture local features, we introduce a perceptual enhancement module (PEM) to improve the interaction of local and global feature information. Second, between the corresponding encoding and decoding blocks in each layer, a feature fusion block (FFB) is introduced to compensate the feature information at different scales to improve the reusability of features and enhance the stability of the network. In addition, between the two stages, the local information features are redistributed and the network supervision capability is improved by introducing a self-calibration module (SCM). In the detail fusion stage, in order to further preserve the details of textural features of the image, we designed a detail enhancement unit (DEU) for recovering high-resolution enhanced images. Through qualitative comparison and quantitative analysis, our method outperforms other low-light image enhancement methods in terms of subjective visual effects and objective metrics values.

关键词： Low-light Image Enhancement Transformer encoder-decoder Attention mechanism

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Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

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WATER 2022年第1期14卷 55-55页

作者： Kusudo, Tsumugu Yamamoto, Atsushi Kimura, Masaomi Matsuno, Yutaka Kindai Univ Fac Agr Dept Environm Management 3327-204 Nakamachi Nara 6318505 Japan

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash-Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.

关键词： reservoir-water level long short-term memory encoder-decoder flood control irrigation water-management tool

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DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

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REMOTE SENSING 2022年第21期14卷 5567页

作者： Liu, Zhiheng Chen, Xuemei Zhou, Suiping Yu, Hang Guo, Jianhua Liu, Yanming Xidian Univ Sch Aerosp Sci & Technol Xian 710026 Peoples R China Tech Univ Munich TUM Dept Aerosp & Geodesy Data Sci Earth Observat D-80333 Munich Germany

Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process;(2) dense blocks are used to extract more spatial features to DUPnet's backbone, increasing feature propagation and reuse;(3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.

关键词： encoder-decoder multi-scale spatial pyramid pooling dense connection regression loss remote sensing water body semantic segmentation

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High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks

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REMOTE SENSING 2019年第21期11卷

作者： Xia, Wei Ma, Caihong Liu, Jianbo Liu, Shibin Chen, Fu Yang, Zhi Duan, Jianbo Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China Sanya Inst Remote Sensing Sanya 572029 Peoples R China China Elect Power Res Inst Co Ltd Beijing 100055 Peoples R China

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and encoder-decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the encoder stage. The details and spatial dimensions of targets are restored using such information during the decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and encoder-decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.

关键词： high-resolution remote sensing image classification deep learning imbalanced data multistage sampling ASPP encoder-decoder

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FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness

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DRONES 2023年第7期7卷

作者： Zhang, Lin Wang, Mingyang Ding, Yunhong Wan, Tingting Qi, Bo Pang, Yutian Northeast Forestry Univ Coll Informat & Comp Engn Harbin 150040 Peoples R China Heilongjiang Inst Construction Technol Dept Informat Engn Harbin 150025 Peoples R China Harbin Inst Technol Sch Astronaut Harbin 150010 Peoples R China Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are widely used in forest fire monitoring tasks due to their flexibility and wide coverage. The key to fire monitoring is to accurately segment the area where the fire is located in the image. However, for early forest fire monitoring, fires captured remotely by UAVs have the characteristics of a small area, irregular contour, and susceptibility to forest cover, making the accurate segmentation of fire areas from images a challenge. This article proposes an FBC-ANet network architecture that integrates boundary enhancement modules and context-aware modules into a lightweight encoder-decoder network. FBC-Anet can extract deep semantic features from images and enhance shallow edge features, thereby achieving an effective segmentation of forest fire areas in the image. The FBC-ANet model uses an Xception network as the backbone of an encoder to extract features of different scales from images. By transforming the extracted deep semantic features through the CIA module, the model's feature learning ability for fire pixels is enhanced, making feature extraction more robust. FBC-ANet integrates the decoder into the BEM module to enhance the extraction of shallow edge features in images. The experimental results indicate that the FBC-ANet model has a better segmentation performance for small target forest fires compared to the baseline model. The segmentation accuracy on the dataset FLAME is 92.19%, the F1 score is 90.76%, and the IoU reaches 83.08%. This indicates that the FBC-ANet model can indeed extract more valuable features related to fire in the image, thereby better segmenting the fire area from the image.

关键词： forest fires semantic segmentation boundary enhancement contextual information awareness encoder-decoder

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Attention-guided image captioning with adaptive global and local feature fusion

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2021年 78卷 103138-103138页

作者： Zhong, Xian Nie, Guozhang Huang, Wenxin Liu, Wenxuan Ma, Bo Lin, Chia-Wen Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Hubei Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan

Although attention mechanisms are exploited widely in encoder-decoder neural network-based image captioning framework, the relation between the selection of salient image regions and the supervision of spatial information on local and global representation learning was overlooked, thereby degrading captioning performance. Consequently, we propose an image captioning scheme based on adaptive spatial information attention (ASIA), extracting a sequence of spatial information of salient objects in a local image region or an entire image. Specifically, in the encoding stage, we extract the object-level visual features of salient objects and their spatial bounding-box. We obtain the global feature maps of an entire image, which are fused with local features and the fused features are fed into the LSTM-based language decoder. In the decoding stage, our adaptive attention mechanism dynamically selects the corresponding image regions specified by an image description. Extensive experiments conducted on two datasets demonstrate the effectiveness of the proposed method.

关键词： Image captioning encoder-decoder Spatial information Adaptive attention

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