Multi-View Structural Feature Extraction for Hyperspectral Image Classification

Liang, Nannan; Duan, Puhong; Xu, Haifeng; Cui, Lin

doi:10.3390/rs14091971

Cited by 15 publications

(10 citation statements)

References 48 publications

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“…This valuable information is combined to obtain a comprehensive decision-making result to improve recognition and interpretation. Feature-level fusion is used to extract the original information from the sensors, and then the feature information is comprehensively analyzed and processed, which can retain more original information [ 89 ]. Constructing a model after fusing features is similar to Section 3.1.3 .…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

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Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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“…CNN-based features outperform handmade or machine learning-based features. N. Liang et al [16] proposes a multi-view structural feature extraction approach to provide a thorough characterization of spectralspatial structures of various objects, which consists mostly of the stages below. First, the original image's spectral number is reduced using the minimum noise fraction (MNF) approach, then the local structural feature is extracted from the dimensionreduced data using a relative total variation.…”

Section: Hybrid Image Fusion Modelsmentioning

confidence: 99%

A versatile hybrid image fusion model to fuse multispectral image data

Ignatious

El-Sayed

Khan

et al. 2022

Preprint

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Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making caused by their poor perceivance of environmental information. In todays, technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context leading to inadequate decision making causing fatal accidents. To overcome this drawback effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks namely data cleaning and data fusion. Since the raw sensory data is complex and exhibits multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focuses on data fusion. In particular, we propose a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposes a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model is proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features are also proposed. The proposed models are evaluated using various standard metrics with existing popular models. The proposed image fusion model performed better than the other models.

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“…Recently, structural profiles have received more attention in the hyperspectral image classification community [ 6 , 19 , 31 , 32 ]. For example, Shah et al used structural profiles in the preprocessing stage to remove texture details, greatly improving the classification performance of the classifier [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Shah et al used structural profiles in the preprocessing stage to remove texture details, greatly improving the classification performance of the classifier [ 31 ]. Liang et al developed a multi-view structural profile to characterize complex spectral–spatial features of hyperspectral images [ 32 ]. These approaches have proven that the structural profile can well represent the intrinsic properties of the input while removing unrelated information.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Wang

Zeng²,

Duan³

et al. 2022

Sensors

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Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.

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Multi-View Structural Feature Extraction for Hyperspectral Image Classification

Cited by 15 publications

References 48 publications

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

A versatile hybrid image fusion model to fuse multispectral image data

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

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