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A vegetation classification method based on improved dual-way branch feature fusion U-net
2022
Frontiers in Plant Science
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Secondly, the depthwise separable convolution and residual connections are combined to replace the common convolution layers of U-Net for depth feature extraction to ensure classification accuracy and ...
Aiming at the problems of complex structure parameters and low feature extraction ability of U-Net used in vegetation classification, a deep network with improved U-Net and dual-way branch input is proposed ...
Zhu et al. proposed a land cover classification method for hyperspectral images based on a fused residual network, which used residual units to learn advanced features with more discriminative power ( ...
doi:10.3389/fpls.2022.1047091
pmid:36523616
pmcid:PMC9745139
fatcat:l5umr5kt7zcshmhkflskegkkwq
Spectral Graph Neural Networks with Manifold-Learning-Based Feature Extraction for Hyperspectral Image Classification
2022
Qeios
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In this short article, we briefly retrospect the recent progress of spectral graph neural networks with manifold-learning-based feature extraction for hyperspectral image classification. ...
Preparation of hyperspectral data for feeding a graph-based deep learning model that draws from the previous strategy how to prepare HSI data for image-based deep learning, is a novel approach in terms ...
In terms of a hyperspectral image classification task, hyperspectral images (HSIs) from regular grids into irregular domains can adapt to the advantages of graph-based deep learning. ...
doi:10.32388/b899uz
fatcat:zn3frzoxkbfphlaj6e3noyhmt4
Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism
2021
Remote Sensing
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network (MRA-NET) that is appropriate for hyperspectral image classification. ...
In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. ...
of the Suggested Deep Classification Technique The proposed deep network architecture uses the PCA algorithm to separate and extract the image space-spectrum features for the first time, and then uses ...
doi:10.3390/rs13030335
fatcat:ajltujwzmrc4ppf55opardwwf4
Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network
2020
Sensors
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By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification ...
Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification ...
Acknowledgments: The authors sincerely thank the Shanghai Science and Technology Commission for their funding support.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20143961
pmid:32708693
pmcid:PMC7412085
fatcat:6owydygp5vag3evdj7plczl3te
Hyperspectral image classification using support vector machines
2020
IAES International Journal of Artificial Intelligence (IJ-AI)
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These BIMFs and residue image is further taken as input to the SVM for classification. ...
In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. ...
The potential capabilities of deep learningbased data management and feature extraction procedures offer wide solutions for HI classifications. ...
doi:10.11591/ijai.v9.i4.pp684-690
fatcat:s3ba4qfkwja7hiopadfoxr2wii
Small Sample Hyperspectral Image Classification Based on Cascade Fusion of Mixed Spatial-Spectral Features and Second-Order Pooling
2022
Remote Sensing
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Then, two 3D spatial-spectral residual modules and one 2D separable spatial residual module are used to extract mixed spatial-spectral features. ...
Hyperspectral images can capture subtle differences in reflectance of features in hundreds of narrow bands, and its pixel-wise classification is the cornerstone of many applications requiring fine-grained ...
Acknowledgments: The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the datasets ...
doi:10.3390/rs14030505
fatcat:4uwpnnqzmfasxeeocivvlnd66m
Hyperspectral Classification of Two-Branch Joint Networks Based on Gaussian Pyramid Multiscale and Wavelet Transform
2022
IEEE Access
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Experimental results on hyperspectral image datasets indicate that the method outperforms other traditional deep learning-based and other advanced classifiers. ...
INDEX TERMS Hyperspectral image, Gaussian pyramid, wavelet transforms, hyperspectral image classification. ...
It is especially suitable to use it to extract hyperspectral image features, because hyperspectral images have many dimensions, even after the PCA algorithm reduces the layer. ...
doi:10.1109/access.2022.3172501
fatcat:45uhpmc4qjgdvb4jsi3cgkaev4
DEEP CONVOLUTION NEURAL NETWORKS WITH RESNET ARCHITECTURE FOR SPECTRAL-SPATIAL CLASSIFICATION OF DRONE BORNE AND GROUND BASED HIGH RESOLUTION HYPERSPECTRAL IMAGERY
2022
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In our research, we propose a deep convolutional neural network architecture (CNN) for the classification of aerial images captured by drones and high-resolution Terrestrial Hyperspectral (THS or HSI) ...
The HSI input layer with corresponding ground truth data for the region is fed into the ResNets model with a spectral and spatial residual network for the 7*7*139 input Hyperspectral Imagery (HSI) volume ...
There are three main approaches for hyperspectral image classification: pixel-based, spectral-spatial-based, and object-based Ding et al. (2020) . ...
doi:10.5194/isprs-archives-xliii-b2-2022-577-2022
fatcat:fbw7ogsqmjgb7fyhxveqlmqodm
Detection and Classification of Early Decay on Blueberry Based on Improved Deep Residual 3D Convolutional Neural Network in Hyperspectral Images
2020
Scientific Programming
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An improved deep residual 3D convolutional neural network (3D-CNN) framework is proposed for hyperspectral images classification so as to realize fast training, classification, and parameter optimization ...
Rich spectral and spatial features can be rapidly extracted from samples of complete hyperspectral images using our proposed network. ...
Spectral spatial-based residual network introduces the residual structure into the 3D-CNN network and uses two 3D convolution kernels of spectral and spatial features to extract deep features, which can ...
doi:10.1155/2020/8895875
fatcat:lwv5gqnp5bbczbhvkjjljdqp6q
Unsupervised deep feature extraction of hyperspectral images
2014
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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Index Terms-Convolutional networks, deep learning, sparse learning, feature extraction, hyperspectral image classification * The work of A. ...
This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. ...
Extracting expressive spatial-spectral features from hyperspectral images is thus of paramount relevance. ...
doi:10.1109/whispers.2014.8077647
dblp:conf/whispers/RomeroGC14
fatcat:2wpbpmki55bevfm22mp3vajmfq
An effective classification method for hyperspectral image with very high resolution based on encoder-decoder architecture
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. ...
Index Terms-Encoder-decoder, hyperspectral image (HSI) with high spatial resolution, image classification, 3-D convolution residual network. interests include remote sensing information processing and ...
They would also like to thank the National Center for Airborne Laser Mapping and the Hyperspectral Image Analysis Laboratory at the University of Houston for acquiring and providing the data used in this ...
doi:10.1109/jstars.2020.3046245
fatcat:n2qr2nm7xnfq3d7clw73o6eppu
Hyperspectral Image Classification Based on Spectral and Spatial Information Using Multi-Scale ResNet
2019
Applied Sciences
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Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. ...
To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. ...
Abbreviations The following abbreviations are used in this manuscript:
HSI Hyperspectral Image PCA Principle Component Analysis CNN Convolutional Neural Network ...
doi:10.3390/app9224890
fatcat:gvdrhblt7vbq3beqfvhzor3rly
Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
2022
Remote Sensing
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for hyperspectral images. ...
However, most classification methods cannot effectively extract dimensionality-reduced data features. ...
This paper mainly focuses on the method of feature extraction based on machine learning for hyperspectral images. ...
doi:10.3390/rs14184579
fatcat:oi7urtfrszfylihomqbhmrsare
Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images
2020
Traitement du signal
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feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP). ...
Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other ...
Residual deep PCA (RDPCA) that are proposed to combine Deep PCA with residual-based multi-scale feature extraction for HSI classification [5] . ...
doi:10.18280/ts.370514
fatcat:jphiutjrkfcfdm2zat64rfjdtm
Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network
2021
Mathematical Problems in Engineering
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Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. ...
First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. ...
Acknowledgments is study was supported by the National Natural Fund Project of Research on Remote Sensing Sea Ice Detection Model of His Source Data Multi-Feature Fusion (no. 42176175), the 13th Five-Year ...
doi:10.1155/2021/9928351
fatcat:zkh3exf7arbqrowqrys7yq5q5u
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