A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi

doi:10.3390/s150715868

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Traditionally, identification models relied on database matching methods 11 . However, with the advancement of artificial intelligence (AI) technology, AI-based identification models have achieved superior performance.…”

Section: Background and Summarymentioning

confidence: 99%

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Lv,

Wang,

Yang

et al. 2024

Sci Data

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The identification technology for coal and coal-measure rock is required across multiple stages of coal exploration, mining, separation, and tailings management. However, the construction of identification models necessitates substantial data support. To this end, we have established a near-infrared spectral dataset for coal and coal-measure rock, which includes the reflectance spectra of 24 different types of coal and coal-measure rock. For each type of sample, 11 sub-samples of different granularities were created, and reflectance spectra were collected from sub-samples at five different detection azimuths, 18 different detection zeniths, and under eight different light source zenith conditions. The quality and usability of the dataset were verified using quantitative regression and classification machine learning algorithms. Primarily, this dataset is used to train artificial intelligence-based models for identifying coal and coal-measure rock. Still, it can also be utilized for regression studies using the industrial analysis results contained within the dataset.

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Section: Background and Summarymentioning

confidence: 99%

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Lv,

Wang,

Yang

et al. 2024

Sci Data

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“…Over the past few decades, hyperspectral imagery has been widely used in different remote sensing applications owing to its high-resolution spectral information of the materials in the scene [ 1 , 2 , 3 ]. Various hyperspectral image classification techniques have been presented for a lot of real applications including material recognition, urban mapping and so on [ 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm

Zhang

et al. 2017

Sensors

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Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and ℓ2,1 norm (SFL) that can deal with all the test pixels simultaneously. The ℓ2,1 norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the ℓ2,1 norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers.

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“…Image matching is a crucial technique with many practical applications in computer vision, including panorama stitching [ 1 ], remote sensing [ 2 ], intelligent video surveillance [ 3 ] and pathological disease detection [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

PIMR: Parallel and Integrated Matching for Raw Data

Yang

Zhao

et al. 2016

Sensors

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With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching algorithm, named parallel and integrated matching for raw data (PIMR). This algorithm not only effectively utilizes the color information of raw data, but also designs a parallel and integrated framework to shorten the time-cost in the demosaicing stage. Experiments show that compared to existing state-of-the-art methods, the proposed algorithm yields a comparable recognition rate, while the total time-cost of imaging and matching is significantly reduced.

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A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

Cited by 4 publications

References 40 publications

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Hyperspectral Image Classification with Spatial Filtering and \(l_{(2,1)}\) Norm

PIMR: Parallel and Integrated Matching for Raw Data

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