Method Study of Mineral Weight Information Extraction Based on Hyperion Hyperspectral Remote Sensing Data - The Region of Gannan as an Example

In this paper, the methods of extracting minerals weight information are studied based on hyperion hyperspectral remote sensing data, taking the region of Gannan area in Jiangxi as an example. After studying spectral angle mapping and matched filtering, the method has been developed which combines them to extract the weight information of minerals. The results show that this method can successfully apply and made spectral angle mapping integrate with matched filtering, combined their advantages and made up their shortcomings, and extract weight information of clay minerals accurately from the background image. Meanwhile, the location of all kinds of mineral and results of mineral mapping are consistent very well, reflecting the application feasibility of the method.

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Comparative Evaluation Threshold Parameters of Spectral Angle Mapper (SAM) for Mapping of Chhabadiya Talc Minerals, Jahajpur, Bhilwara, India using Hyperion hyperspectral Remote Sensing Data

2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT) ◽

10.1109/icct46177.2019.8969015 ◽

2019 ◽

Author(s):

Mahesh Kumar Tripathi ◽

Himanshu Govil ◽

Prabhat Diwan

Keyword(s):

Remote Sensing ◽

Comparative Evaluation ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Spectral Angle Mapper ◽

Sensing Data ◽

Spectral Angle

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Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

10.1117/12.712738 ◽

2006 ◽

Author(s):

Hong-gen Xu ◽

Hong-chao Ma ◽

De-ren Li ◽

Yan Song

Keyword(s):

Remote Sensing ◽

Information Extraction ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Sensing Data ◽

Thematic Information

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Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network

PLoS ONE ◽

10.1371/journal.pone.0254542 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0254542

Author(s):

Zhengyang Wang ◽

Shufang Tian

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Information Extraction ◽

Remote Sensing Data ◽

Kappa Coefficient ◽

Hyperspectral Remote Sensing ◽

Classification Model ◽

Backpropagation Neural Network ◽

Sensing Data

The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.

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