Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery

Li, Na; Huang, Xinchen; Zhao, Haitao; Qiu, Xianfei; Geng, Ruonan; Jia, Xiuping; Wang, Daming

doi:10.1109/jstars.2018.2814617

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…Holloway and Mengersen (2018) Finally, geological, remote sensing, and geochemical data were efficiently integrated into the CG-LR model to predict skarn deposits. Li et al (2018) presented a multivariate regression model based on hyperspectral imaging to quantitatively analyze and predict the device capability and optimize device parameters for detecting minerals and geological survey.…”

Section: Regressionmentioning

confidence: 99%

A review of machine learning in processing remote sensing data for mineral exploration

Shirmard,

Farahbakhsh,

Muller

et al. 2021

Preprint

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As a primary step in mineral exploration, a variety of features are mapped such as lithological units, alteration types, structures, and minerals. These features are extracted to aid decision-making in targeting ore deposits. Different types of remote sensing data including satellite optical and radar, airborne, and drone-based data make it possible to overcome problems associated with mapping these important parameters on the field. The rapid increase in the volume of remote sensing data obtained from different platforms has allowed scientists to develop advanced, innovative, and powerful data processing methodologies. Machine learning methods can help in processing a wide range of remote sensing data and in determining the relationship between the reflectance continuum and features of interest. Moreover, these methods are robust in processing spectral and ground truth measurements

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Section: Regressionmentioning

confidence: 99%

A review of machine learning in processing remote sensing data for mineral exploration

Shirmard,

Farahbakhsh,

Muller

et al. 2021

Preprint

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show abstract

“…Images from multispectral and hyperspectral sensors have found wide applications, ranging from mining [1], oceanography [2], agriculture [3], meteorological studies [4], geological observations [5], to name a few. Multi-spectral satellites consist of several spectral bands which image the land surface with multiple spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

Chaudhuri

Dey

Datcu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Conventional remote sensing data analysis techniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This work exploits the contextual information capturing ability of deep neural networks (DNNs), particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to non-linear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multi-spectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multi-labeled images and use it to design an inter-band group retrieval framework. We demonstrate its effectiveness over the conventional tripletloss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.

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“…Hyperspectral imagery (HSI), with hundreds of narrow bands, can provide more abundant spectral information than other remote sensing approaches, such as infrared images and multispectral images [1]. Exploiting this property, HSI shows its advantage in classification, unmixing, and target detection [2][3][4], which has been widely employed in many fields, including intelligent agriculture, mineral exploration, and military applications [5][6][7][8][9][10]. As an essential part in hyperspectral image analysis, anomaly detection aims at identifying targets in an unsupervised manner, which possesses the following characteristics: (1) the spectral curves of anomalies are different from those of the surrounding background, and they only occupy a tiny part of the entire image; (2) no spectral information about background or targets are known in advance.…”

Section: Introductionmentioning

confidence: 99%

FrHPI: A Discriminative Patch-Image Model for Hyperspectral Anomaly Detection

Fan

Zeng

et al. 2021

Mathematical Problems in Engineering

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Anomaly detection is now a significantly important part of hyperspectral image analysis to detect targets in an unsupervised manner. Traditional hyperspectral anomaly detectors fail to consider spatial information, which is vital in hyperspectral anomaly detection. Moreover, they usually take the raw data without feature extraction as input, limiting the detection performance. We propose a new anomaly detector based on the fractional Fourier transform (FrFT) and a modified patch-image model called the hyperspectral patch-image (HPI) model to tackle these two problems. By combining them, the proposed anomaly detector is named fractional hyperspectral patch-image (FrHPI) detector. Under the assumption that the target patch-image is a sparse matrix while the background patch-image is a low-rank matrix, we first formulate a matrix by sliding a rectangle window on the first three principal components (PCs) of HSI. The matrix can be decomposed into three parts representing the background, targets, and noise with the well-known low-rank and sparse matrix decomposition (LRaSMD). Then, distinctive features are extracted via FrFT, a transformation which is desirable for noise removal. Background atoms are selected to construct the covariance matrix. Finally, anomalies are picked up with Mahalanobis distance. Extensive experiments are conducted to verify the proposed FrHPI detector’s superiority in hyperspectral anomaly detection compared with other state-of-the-art detectors.

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Multiparameter Optimization for Mineral Mapping Using Hyperspectral Imagery

Cited by 11 publications

References 27 publications

A review of machine learning in processing remote sensing data for mineral exploration

A review of machine learning in processing remote sensing data for mineral exploration

Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive

FrHPI: A Discriminative Patch-Image Model for Hyperspectral Anomaly Detection

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