A Survey on Hyperspectral Imaging for Mineral Exploration using Machine Learning Algorithms

Sudharsan, S.; Hemalatha, R.; Radha, S.

doi:10.1109/wispnet45539.2019.9032740

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…In recent years, many scholars globally have conducted in-depth research on hyperspectral anomaly detection. Hyperspectral anomaly detection technology has rapidly developed, and in addition to image classification, it is also widely used in mineral exploration [ 12 , 13 ], ground object classification [ 14 ], ecological monitoring [ 15 , 16 ], military reconnaissance [ 17 , 18 ], border monitoring and search and rescue [ 11 ] and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Lian

Zhao

Zheng

et al. 2023

Sensors

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Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in complex scenes with multiple background edges and noise. Therefore, this study proposes a weighted sparse hyperspectral anomaly detection method. First, using the idea of matrix decomposition in mathematics, the original hyperspectral data matrix is reconstructed into three sub-matrices with low rank, small sparsity and representing noise, respectively. Second, to suppress the noise interference in the complex background, we employed the low-rank, background image as a reference, built a local spectral and spatial dictionary through the sliding window strategy, reconstructed the HSI pixels of the original data, and extracted the sparse coefficient. We proposed the sparse coefficient divergence evaluation index (SCDI) as a weighting factor to weight the sparse anomaly map to obtain a significant anomaly map to suppress the background edge, noise, and other residues caused by decomposition, and enhance the abnormal target. Finally, abnormal pixels are segmented based on the adaptive threshold. The experimental results demonstrate that, on a real-scene hyperspectral dataset with a complicated background, the proposed method outperforms the existing representative algorithms in terms of detection performance.

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Section: Introductionmentioning

confidence: 99%

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Lian

Zhao

Zheng

et al. 2023

Sensors

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“…Hyperspectral Imaging (HSI) is an enabling technology for a variety of remote sensing applications related to intelligence, commerce, agriculture, military, and even humanitarian purposes. Such applications include environmental monitoring [ 1 ], agricultural field observation [ 2 ], geological mapping [ 3 ], and mineral exploration [ 4 ], to name just a few. It has been steadily growing over the last few years.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Altamimi

Youssef

2021

Sensors

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Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

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“…Although historically HSI has been applied to remote sensing [3], in recent years this technology has become a trending topic in different research fields such as food quality analysis [4,5], military and security applications [6] or agriculture [7,8], among many others [9]. HSI is also an emerging imaging modality in the medical field.…”

Section: Introductionmentioning

confidence: 99%

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Ortega¹,

Halicek²,

Fabelo³

et al. 2021

Multimedia Information Retrieval

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Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

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A Survey on Hyperspectral Imaging for Mineral Exploration using Machine Learning Algorithms

Cited by 19 publications

References 11 publications

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

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