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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

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Learning-based Classification Approach for Coded Aperture Compressive Spectral image

Learning-based Classification Approach for Coded Aperture Co...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Wang, Peng Ma, Xu Zhao, Qile Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510666351

Spectral images include rich spatio-spectral information of target scene, which can accurately identify and distinguish the features of ground objects. Therefore, spectral image classification is widely used in remote sensing. However, traditional spectral imaging techniques need to scan the region of interest along the spatial dimension or spectral dimension, which takes a long acquisition time and increases the burden of data transmission and storage. To overcome these shortcomings, coded aperture snapshot spectral imaging (CASSI) system based on compressive sensing theory appeared. In this paper, we build a testbed of dual-disperser CASSI (DD-CASSI) system, which can reconstruct the three-dimensional (3D) spectral image datacube of target object from a few two-dimensional compressive measurements. Then, a 3D convolutional neural network is applied to accomplish the spectral image classification based on the reconstructed datacube. Different classification methods are compared based on the experimental data. It shows that the proposed compressive spectral image classification method achieves pretty close results compared to the classification methods based on the original datacube. But, the proposed method is beneficial to improve the acquisition efficiency of the spectral image data. © 2023 SPIE.

关键词： image classification

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Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from remote sensing images 28

Advanced Deep Learning Architectures for Accurate Detection ...

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conference on image and signal processing for remote sensing XXVIII

作者： Breitkopf, Tom Lukas Hackel, Leonard Ravanbakhsh, Mahdyar Cooke, Anne-Karin Willkommen, Sandra Broda, Stefan Demir, Beguem Tech Univ Berlin D-10623 Berlin Germany Bundesanstalt Geowissensch & Rohstoffe Berlin D-13593 Berlin Germany

ISBN: (纸本)9781510655386;9781510655379

Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provide an entryway of agrochemicals into subsurface water bodies and increase nutrition loss in soils. For maintenance and infrastructural development, accurate maps of tile drainage pipe locations and drained agricultural land are needed. However, these maps are often outdated or not present. Different remote sensing (RS) image processing techniques have been applied over the years with varying degrees of success to overcome these restrictions. Recent developments in deep learning (DL) techniques improve upon the conventional techniques with machine learning segmentation models. In this study, we introduce two DL-based models: i) improved U-Net architecture;and ii) Visual Transformer-based encoder-decoder in the framework of tile drainage pipe detection. Experimental results confirm the effectiveness of both models in terms of detection accuracy when compared to a basic U-Net architecture. Our code and models are publicly available at https://***/rsim/drainage-pipes-detection.

关键词： Semantic segmentation tile drainage pipe detection visual transformer U-Net remote sensing

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GLOBAL EVOLUTION NEURAL NETWORK FOR SEGMENTATION OF remote sensing imageS 47

GLOBAL EVOLUTION NEURAL NETWORK FOR SEGMENTATION OF REMOTE S...

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47th IEEE International conference on Acoustics, Speech and signal processing (ICASSP)

作者： Geng, Xinzhe Lei, Tao Chen, Qi Su, Jian He, Xi Wang, Qi Nandi, Asoke K. Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Shaanxi Joint Lab Artificial Intelligence Xian 710021 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

ISBN: (纸本)9781665405409

The popular convolutional neural networks (CNNs) have been successfully used in very high-resolution remote sensing image semantic segmentation. However, these networks often suffer from performance limitations. First, although deeper networks usually provide better feature representation, they may cause parameter redundancy and the inefficient use of prior knowledge. Secondly, attention-based networks often only focus on weighting different features of a single sample but ignore the correlation of all samples in training set, thus leading to the loss of global information. To address above issues, we propose two simple yet effective global evolution strategies. The first is knowledge enhancement. This strategy can reactivate invalid convolutional kernels through convergence of different models and make full use of prior knowledge from the network to improve its feature representation. The second is a dict-attention module that greatly enhances the generalization of networks by learning and inferring the global relationship among different samples through the dictionary unit. As a result, a novel global evolution network (GENet) is designed based on knowledge enhancement and dict-attention for remote sensing image semantic segmentation. Experiments demonstrate that the proposed GENet is not only superior to popular networks in segmentation accuracy.

关键词： Deep learning image segmentation knowledge enhancement attention mechanism

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RGConvNET:A Hybrid Hyperspectral image Classification Model for Extracting Deep Features with Improved Accuracy 1

RGConvNET:A Hybrid Hyperspectral Image Classification Model ...

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1st IEEE International conference on Advances in signal processing, Power, Communication, and Computing, ASPCC 2024

作者： Ranjan, Vikash Biswal, Pradyut Kumar Iiit Bhubaneswar Department of Electronics and Telecommunication Bhubaneswar India

ISBN: (纸本)9798350355093

Hyperspectral imaging (HSI) has an important role in applications like urban planning, agriculture, surveillance, and remote sensing, where it helps identify various regions and materials. Although convolutional neural networks (CNNs) have significant attainment in feature extraction and classification, they face limitations in handling the dimensionality of HSI data and cannot efficiently extract local features under geometric transformations. In order to resolve this, we suggest a hybrid HSI classification model combining Recursive Gated Convolution (RG-Convolution) and U-Net. Three RG-Convolution blocks are used to capture spatial features, while U-Net handles spectral information. A dense network with global average pooling then maps the features to different classes, reducing dimensionality. The model, tested on benchmark datasets like Pavia University and Indian Pines, achieves superior performance compared to existing methods, with an overall accuracy of 98.56% and 99.84% for Indian Pines and Pavia University dataset, respectively. This outperforms current state-of-the-art algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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Tele-Rheumatology-Advanced Neural Network Model for remote Assessment and Management of Rheumatic Conditions 10

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

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10th International conference on Communication and signal processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Forest canopy information extraction in Gannan Plateau based on UAV high-resolution images

Forest canopy information extraction in Gannan Plateau based...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Wu, Yanduo Han, Hui Faculty of Geomatics Lanzhou Jiaotong University Lanzhou730070 China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou730070 China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou730070 China

ISBN: (纸本)9781510666351

In order to explore the feasibility of applying UAV remote sensing and object-oriented canopy extraction technology to forest areas with different canopy densities, 26 forest plots located in Gannan Plateau were selected and divided into two categories of coniferous forests with medium and high canopy densities. ESP tool was used to screen the optimal segmentation scale for multi-scale segmentation, and the feature space was optimized for object-oriented classification. The east-west and north-south crown amplitudes on quadrat scale were extracted in ArcGIS. The extracted crown amplitude was compared with the field measured data to evaluate the accuracy. The results were as follows: In UAV image extraction, the fitting values R2 and RMSE of east and west canopy width and measured values were 0.865 and 1.025m, respectively, and the fitting values R2 and RMSE of north and south canopy width were 0.867 and 1.235m, respectively. The R2 and RMSE of east and west canopy width with high canopy closure were 0.828 and 1.145m, respectively, and the R2 and RMSE of north and south canopy width were 0.787 and 1.523m, respectively. The extraction effect of the plots with medium canopy closure was better than that of the plots with high canopy closure, and both of the two canopy closure met the accuracy requirements. The results show that the automatic extraction method of canopy parameter information based on UAV remote sensing image is efficient and reliable, and has important practical significance in forest resources investigation. © 2023 SPIE.

关键词： remote sensing

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Affine Non-negative Representation for Few-Shot remote sensing Scene Classification 16

Affine Non-negative Representation for Few-Shot Remote Sensi...

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16th IEEE International conference on signal processing (ICSP)

作者： Du, Chunyu Liu, Baodi Wang, Yanjiang China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Shandong Peoples R China

ISBN: (数字)9781665460569

ISBN: (纸本)9781665460569

It is hard to acquire plenty of labeled samples in remote sensing due to the limitations of the imaging environment and the imaging equipment. Recently, few-shot learning, which attempts to identify invisible categories with restricted labeled samples, has shown tremendous potential to address this issue. The remote sensing Scene Classification (RSSC) based on few-shot learning generally involves two steps: the pre-training phase and the meta-test phase. Nevertheless, since the sample categories in these two stages are disjoint and only one or a few labeled samples per class are given for training in the meta-testing phase, which causes an "overfitting" problem and thus limits the model's generalization ability. To overcome the challenge, we propose an Affine Non-negative Representation for Few-Shot RSSC (ANR-RSSC). Specifically, we design a representation-based classifier with affine non-negative constraints to prevent "overfitting" by reducing model parameters. Further, the proposed method maps the extracted features into Radial Basis Function (RBF) kernel space to rehabilitate more discriminating features making the data more linearly separable. Extensive experiments on NWPU-RESISC45 and RSD46-WHU remote sensing image datasets show the merits of the proposed method.

关键词： few-shot learning remote sensing scene classification affine non-negative constraints kernel function

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A Strong Scattering Solution with Applications to remote sensing Systems Modelling 34

A Strong Scattering Solution with Applications to Remote Sen...

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34th Irish signals and Systems conference (ISSC)

作者： Blackledge, Jonathan Technol Univ Dublin Sch Elect & Elect Engn Dublin Ireland

ISBN: (纸本)9798350340570

Although not always fully appreciated as such, the linear stationary convolution model for a signal and/or image, generated by the interaction of an incident wave field with an inhomogeneous medium, is based on the 'weak scattering condition', commonly referred to as the 'Born approximation'. This approximation forms the basis for signal processing and analysis associated with applications over a broad range of frequencies. The Born approximation assumes that a scattering interaction is based on single scattering events alone and that the scattered field is therefore a weak field in comparison to the incident field. The physical limitations of such a model are usually overcome by introducing an additional stochastic field term which takes into account all effects that do not conform to the Born approximation, coupled with background 'system noise'. In this paper, a solution to the 'scattering problem' for scalar fields is presented, which does not explicitly rely on the weak scattering condition. It is shown how, for side-band systems, this solution can be cast in terms of a linear stationary model, thereby allowing standard signal analysis and processing methods to be adopted. A case study is presented based on the application of a self-affine model for the scattering function to simulate Synthetic Aperture Radar images.

关键词： signals Modelling Scattering Solutions Fractal Scattering Functions Synthetic Aperture Radar

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A Review on Estimation of soil Macronutrients using Satellite image processing 2

A Review on Estimation of soil Macronutrients using Satellit...

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2nd National conference on signal processing, Sustainable Energy Materials and Astronomy and Astrophysics, NSSEMA 2023

作者： Bera, Swagota Shrivastava, Arti Agrawal, Himani Mandal, Sunandal SSTC SSGI Bhilai India Govt. Engineering College Raipur India Govt. Polytechnique College Durg India SOS in Electronics and Photonics Pt. R. S. University Raipur India

Major portion of Indian economy depends on agriculture. More than 40 % of the Indian land is used for agriculture which contributes to Gross Domestic Product (GDP) of the country and also provides employ to the population of the country. Increment in the population day by day increases the requirement for the increment in the yield of the agricultural products. There are several factors which is responsible to control the agricultural product yield. Among them Nitrogen, Phosphorus and Potassium are the macronutrients are the vital elements of the soil . Due to the instability in the climatic condition of India and also due to other biotic and abiotic factors macronutrients get varies. To increase the crop yield, the perfect handling of macronutrients i.e., nitrogen, phosphorus and potassium are required. As the technology is developing day by day, the application of remote sensing in agriculture is becoming promising. The satellite images of agricultural land can be processed and analysed to provide vital information for efficient agricultural practices. This paper is the review of the present state of art in the field of satellite image processing techniques for the estimation of the macronutrients of the agricultural land. This review will promote the acceptance of hyperspectral image technology for the investigation of soil NPK. © Published under licence by IOP Publishing Ltd.

关键词： remote sensing

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remote sensing image Mosaic Technology Based on Improved SIFT Algorithm 11th

Remote Sensing Image Mosaic Technology Based on Improved SIF...

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11th International conference on Communications, signal processing, and Systems, CSPS 2022

作者： Wang, Meng Wang, Feng-kun Anhui Technical College of Mechanical and Electrical Engineering Anhui Wuhu241002 China

ISBN: (纸本)9789819926527

To solve the problem of a large amount of computation and low efficiency of the traditional remote sensing image mosaic algorithm, an Improved SIFT feature matching image mosaic algorithm is proposed. Aiming at the disadvantage of too many dimensions of feature point descriptors in SIFT algorithm, a dimension reduction scheme is proposed. Centered on the feature point, concentric circles are constructed with the radius of 2 pixels, 4 pixels and 6 pixels respectively. The region is divided according to the distance from the feature point. If the distance is close, the weight is high, and if the distance is far, the weight is low. The region is divided into 4, 8, and 16 partitions, so as to generate a 56-dimensional descriptor. Furthermore, the feature points in the image matching are divided into two data sets, namely, maximum and minimum. The best matching point is selected by calculating the distance between the maximum and minimum, which reduces the amount of calculation of image matching and saves the algorithm time. Finally, the weighted smoothing method is used for image mosaic. Experimental tests were carried out on the three situations of remote sensing image scaling, rotation, and gray change. The results show that the proposed algorithm can effectively reduce the calculation amount and accelerate the stitching speed, and the stitching effect is ideal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pixels

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