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BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Anirban Santara, Kaustubh Mani, Pranoot Hatwar, Ankit Singh, Ankur Garg, Kirti Padia, Pabitra Mitra
2017 IEEE Transactions on Geoscience and Remote Sensing  
Deep learning based landcover classification algorithms have recently been proposed in literature.  ...  The method is found to outperform the highest reported accuracies on popular hyperspectral image data sets.  ...  Among deep learning based classifiers, a N c -150-100-50-C Multi-Layer Perceptron (MLP), the Convolutional Neural Network (CNN) architecture of Hu et al. [27] and the Convolutional Neural Network with  ... 
doi:10.1109/tgrs.2017.2705073 fatcat:nkg5lzfly5gpxd33drr6jnspm4

Transformers in Remote Sensing: A Survey [article]

Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz khan
2022 arXiv   pre-print
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade.  ...  Recently, transformers-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement  ...  Convolutional Neural Networks Convolutional neural networks (CNNs) have dominated a variety of computer vision tasks, including image classification [23] and object detection [24] .  ... 
arXiv:2209.01206v1 fatcat:luchmgyyord5nnjthywqszyj6q

Hierarchical Shrinkage Multi-Scale Network for Hyperspectral Image Classification with Hierarchical Feature Fusion

Hongmin Gao, Zhonghao Chen, Chenming Li
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Hyperspectral image classification (HSIC), convolutional neural network (CNN), hierarchical shrinkage multi-scale network (HSMSN), multi-depth and multi-scale residual block (MDMSRB), hierarchical  ...  Recently, deep learning (DL) based hyperspectral image classification (HSIC) has attracted substantial attention.  ...  Therefore, a series of hyperspectral image classification methods based on dilated convolution are proposed [54] , [55] .  ... 
doi:10.1109/jstars.2021.3083283 fatcat:vgnfuwahxvganblzrjjabmtwc4

Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Yuhao Qing, Wenyi Liu
2021 Remote Sensing  
network (MRA-NET) that is appropriate for hyperspectral image classification.  ...  Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms  ...  The neural network model extracts image feature information.  ... 
doi:10.3390/rs13030335 fatcat:ajltujwzmrc4ppf55opardwwf4

Hyperspectral Image Super-Resolution with 1D–2D Attentional Convolutional Neural Network

Jiaojiao Li, Ruxing Cui, Bo Li, Rui Song, Yunsong Li, Qian Du
2019 Remote Sensing  
To tackle this challenging task, different from the existing learning-based HSISR algorithms, in this paper we propose a novel framework, i.e., a 1D–2D attentional convolutional neural network, which employs  ...  Compared with the typical 3D convolutional neural network (CNN), the 1D–2D CNN is easier to train with less parameters.  ...  To ensure that both tasks can employ this network, the pan image used for hyperspectral pansharpening is simply addressed via a multi-scale learning network to extract deep spatial features, and then form  ... 
doi:10.3390/rs11232859 fatcat:btvw2fyv4vbs7oqzwjrj6n2uj4

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4325-4338 A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification.  ...  ., +, JSTARS 2020 1271-1285 A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

HResNetAM: Hierarchical Residual Network with Attention Mechanism for Hyperspectral Image Classification

Zhixiang Xue, Xuchu Yu, Liu Bing, Xiong Tan, Xiangpo Wei
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The straightforward convolutional neural network based models have limitations in exploiting the multi-scale spatial and spectral features, and this is the key factor in dealing with the high-dimensional  ...  Index Terms-hyperspectral image, hierarchical residual network, attention mechanism, double branch structure, spectralspatial classification.  ...  Paolo Gamba for providing the Pavia Centre data, the ISPRS for providing the Dioni data and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for providing the Houston 2013 and Houston 2018  ... 
doi:10.1109/jstars.2021.3065987 fatcat:lxvvaiverrhjjfsm6hijrb4lja

Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images

Lixia Wang, Aditya Sole, Jon Yngve Hardeberg
2022 Remote Sensing  
In the last several years, deep learning has been introduced to recover a hyperspectral image (HSI) from a single RGB image and demonstrated good performance.  ...  In particular, attention mechanisms have further strengthened discriminative features, but most of them are learned by convolutions with limited receptive fields or require much computational cost, which  ...  [25] proposed two advanced convolution neural networks (HSCNN-R and HSCNN-D) for a hyperspectral reconstruction task.  ... 
doi:10.3390/rs14133128 fatcat:ne6vqtlt2rb7hedm3jauxs22oe

Special issue on extreme learning machine and deep learning networks

Zhihong Man, Guang-Bin Huang
2020 Neural computing & applications (Print)  
In ''A multi-target corner pooling-based neural network for vehicle detection'', the authors propose a novel convolutional neural network based on multi-target corner pooling layers.  ...  In ''Hyperspectral image super-resolution using recursive densely convolutional neural network with spatial constraint strategy'', the authors propose a compact deep network for HSI super-resolution (SR  ... 
doi:10.1007/s00521-020-05175-0 fatcat:4a6v6gptyzhy5ncwnmhuvhgwqq

Multi-branch Selective Kernel Networks for Hyperspectral Image Classification

T. Alipour, M. E. Paoletti, J. M. Haut, H. Arefi, J. Plaza, A. Plaza
2022 Zenodo  
Convolutional neural networks (CNNs) have demonstrated excellent performance in hyperspectral image (HSI) classification.  ...  In this letter, a new Multi-branch Selective Kernel Network (MSKNet) is introduced, in which the input image is convolved using different RF sizes to create multiple branches, so that the effect of each  ...  General flowchart of the proposed multi-branch selective kernel network (MSKNet) and the selective kernel building block (SKunit) for hyperspectral image (HSI) classification.  ... 
doi:10.5281/zenodo.6413915 fatcat:eups6ia3rzgnzhah3f3yc563f4

Densely Connected Multi-scale Attention Network for Hyperspectral Image Classification

Hongmin Gao, Yawen Miao, Xueying Cao, Chenming Li
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Attention mechanism, convolutional neural network (CNN), dense connectivity, hyperspectral image (HSI) classification, multiscale features.  ...  In recent years, the hyperspectral classification method based on convolutional neural networks has demonstrated excellent performance.  ...  recurrent neural networks [24] , [25] , and graph convolutional networks [26] .  ... 
doi:10.1109/jstars.2021.3056124 fatcat:d45bsrupsvas3fnpe25vhamine

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

César Cheuque, Marvin Querales, Roberto León, Rodrigo Salas, Romina Torres
2022 Diagnostics  
Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level.  ...  Most machine learning tools make a one-level classification for white blood cell classification.  ...  [35] Three-dimensional attention networks for hyperspectral images. Yao et al. [38] Two-module weighted optimized deformable convolutional neural networks.  ... 
doi:10.3390/diagnostics12020248 pmid:35204339 pmcid:PMC8871319 fatcat:qoju3ajin5gink4qemxoaosqlu

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2021 7857-7868 Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks.  ...  ., +, JSTARS 2021 10977-10989 Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks.  ...  ., Hyperspectral Image Superresolution via Deep Structure and Texture Interfusion; JSTARS 2021 8665-8678 Hu, J., see Feng, D., JSTARS 2021 12212-12223 Hu, J., Shen, X., Yu, H., Shang, X., Guo, Q.,  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

DPSA: dense pixelwise spatial attention network for hatching egg fertility detection

Lei Geng, Yunyun Xu, Zhitao Xiao, Jun Tong
2020 Journal of Electronic Imaging (JEI)  
Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our  ...  Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our  ...  Ultrasonic image-based detection 2 soon followed, which led to hyperspectral imaging technology.  ... 
doi:10.1117/1.jei.29.2.023011 fatcat:n3iibcvw75aatkit6r7pvuqicm

A review of neural networks in plant disease detection using hyperspectral data

Kamlesh Golhani, Siva K. Balasundram, Ganesan Vadamalai, Biswajeet Pradhan
2018 Information Processing in Agriculture  
A B S T R A C T This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection.  ...  Then we highlight the current state of imaging and nonimaging hyperspectral data for early disease detection.  ...  (PNN), and Convolutional Neural Network (CNN).  ... 
doi:10.1016/j.inpa.2018.05.002 fatcat:gwoo3nwdwrdgvmeinlcfge6mma
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