Semisupervised Feature Extraction of Hyperspectral Image Using Nonlinear Geodesic Sparse Hypergraphs

Duan, Yule; Hong, Huixiao; Wang, Tao

doi:10.1109/tgrs.2021.3110855

Cited by 66 publications

(17 citation statements)

References 40 publications

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“…This has led to its use in remote sensing applications in fields such as as defense [ 1 ], security [ 2 ] or mineral identification [ 3 ] just to name a few, as well as in controlled environments such as in laboratories to conduct experiments and studies of particular materials and products [ 4 , 5 ] or in industrial processes, contributing to the screening of the quality of goods in production [ 6 ]. Hyperspectral image processing has been a topic of deep research over the last few decades, as numerous new techniques emerge, from simple spectral index calculations to complex deep learning algorithms, with the purpose of finding a trade-off between results improvements and operations and data simplification [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Morales

Horstrand

Guerra

et al. 2022

Sensors

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Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.

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Section: Introductionmentioning

confidence: 99%

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Morales

Horstrand

Guerra

et al. 2022

Sensors

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“…Dimensionality reduction is an indispensable part of modern data processing tasks due to the massive amount of information provided by pervasive means of sensing and storage [1]. Instances of high-dimensional processing tasks are hyperspectral and medical image processing, data visualization, recognition, and classification [2].…”

Section: Introductionmentioning

confidence: 99%

Low‐rank isomap algorithm

Mehrbani

Kahaei

2022

IET Signal Processing

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Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the computational complexity of the graphing stage has been investigated by graph processing methods, the eigenvalue decomposition stage remains a bottleneck in the problem. In this paper, we propose the Low‐Rank Isomap (LRI) algorithm by introducing a projection operator on the embedded graph from the ambient space to a low‐rank latent space to facilitate applying the partial eigenvalue decomposition. This approach leads to reducing the complexity of Isomap to a linear order while preserving the structural information during the dimensionality reduction process as long as the number of observations remains extensively larger than the dimensionality of the ambient space. The superiority of the LRI algorithm compared to some state‐of‐art algorithms is experimentally verified on facial image clustering in terms of speed and accuracy.

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“…Imaging spectrometer can collect data simultaneously in hundreds of narrow and contiguous spectral bands, thus providing both comprehensive spectral and spatial distribution information derived from the neighbouring pixels [1,2]. The hyperspectral image (HSI) is actually a 3-D data cube that can effectively interpret the features contained in the observed region and improve the classification and monitoring abilities of the material in the image [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of hyperspectral images via improved cycle‐MLP

Gong¹,

Zhang²,

Zhou

et al. 2022

IET Computer Vision

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Pixel-wise classification of hyperspectral image (HSI) is a hot spot in the field of remote sensing. The classification of HSI requires the model to be more sensitive to dense features, which is quite different from the modelling requirements of traditional image classification tasks. Cycle-Multilayer Perceptron (MLP) has achieved satisfactory results in dense feature prediction tasks because it is an expert in extracting high-resolution features. In order to obtain a more stable receptive field and enhance the effect of feature extraction in multiple directions, we propose an MLP-like model called DriftNet for HSI classification inspired by Cycle-MLP and deformable convolution. Besides, the proposed DriftNet uses a unique ladder-like fully connected structure to achieve progressive activation of neurons and facilitates the fusion of spatial and spectral information, thereby obtaining more sensitive location information for better classification results. Experimental results on three public hyperspectral datasets demonstrate the effectiveness and generalisation of DriftNet. K E Y W O R D S cycleMLP, classification of hyperspectral image, deep learning, driftFCThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Semisupervised Feature Extraction of Hyperspectral Image Using Nonlinear Geodesic Sparse Hypergraphs

Cited by 66 publications

References 40 publications

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Low‐rank isomap algorithm

Classification of hyperspectral images via improved cycle‐MLP

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