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Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang and Bin Li
Hyperspectral images (HSIs) are pivotal in various fields due to their rich spectral?spatial information. While convolutional neural networks (CNNs) have notably enhanced HSI classification, they often generate redundant spatial features. To address this...
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Jun Wu, Xinyi Sun, Lei Qu, Xilan Tian and Guangyu Yang
Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled informatio...
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Chaitali Bhattacharyya and Sungho Kim
With the development of new technologies inside car mechanisms with various sensors connected to the IoT, a new generation of automation is attracting attention. However, there are still some factors that are difficult to detect. Among them, one of the h...
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Guochao Zhang, Weijia Cao and Yantao Wei
With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixe...
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Jihyoung Ryu and Yeongmin Jang
Convolution neural networks have received much interest recently in the categorization of hyperspectral images (HSI). Deep learning requires a large number of labeled samples in order to optimize numerous parameters due to the expansion of architecture d...
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Zhihua Wang, Zhan Zhao and Chenglong Yin
The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the p...
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Jinn-Min Yang, Shih-Hsuan Wei
Pág. 68 - 72
Feature extraction (FE) or dimensionality reduction (DR) plays quite an important role in the field of pattern recognition. Feature extraction aims to reduce the dimensionality of the high-dimensional dataset to enhance the classification accuracy and fo...
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Konstantinos Demertzis and Lazaros Iliadis
Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely cos...
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Lin He, Xianjun Chen, Jun Li and Xiaofeng Xie
Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., ir...
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Zong-Yue Wang, Qi-Ming Xia, Jing-Wen Yan, Shu-Qi Xuan, Jin-He Su and Cheng-Fu Yang
In this paper, a multi-scale ResNet is proposed for hyperspectral image classification, which can be applied in biohazard detection, agriculture, wasteland fire tracking, and environmental science.
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Junli Yang, Zhiguo Jiang, Shuang Hao and Haopeng Zhang
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Jianjun Liu, Zhiyong Xiao, Yufeng Chen and Jinlong Yang
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Shiuan Wan, Mei-Ling Yeh and Hong-Lin Ma
Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the re...
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Mohamed Ismail and Milica Orlandic
Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme exp...
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Razieh Pourdarbani, Sajad Sabzi, Mohsen Dehghankar, Mohammad H. Rohban and Juan I. Arribas
The presence of bruises on fruits often indicates cell damage, which can lead to a decrease in the ability of the peel to keep oxygen away from the fruits, and as a result, oxygen breaks down cell walls and membranes damaging fruit content. When chemical...
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Zifan Rong, Xuesong Jiang, Linfeng Huang and Hongping Zhou
Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs...
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Yifan Si, Dawei Gong, Yang Guo, Xinhua Zhu, Qiangsheng Huang, Julian Evans, Sailing He and Yaoran Sun
DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dim...
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Loganathan Agilandeeswari, Manoharan Prabukumar, Vaddi Radhesyam, Kumar L. N. Boggavarapu Phaneendra and Alenizi Farhan
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even...
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Hamail Ayaz, Muhammad Ahmad, Ahmed Sohaib, Muhammad Naveed Yasir, Martha A. Zaidan, Mohsin Ali, Muhammad Hussain Khan and Zainab Saleem
Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on la...
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Sowmya Natesan, Costas Armenakis, Guy Benari and Regina Lee
Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectromet...
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