Fig 5 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Performance comparison of different extraction models. https://doi.org/10.1371/journal.pone.0254542.g005
Source publication
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 ar...
Similar publications
Wildfires pose a direct threat when occurring close to populated areas. Additionally, their significant carbon and climate feedbacks represent an indirect threat on a global, long-term scale. Monitoring and analyzing wildfires is therefore a crucial task to increase the understanding of interconnections between fire and ecosystems, in order to impr...
Citations
... Due to differences in compositional and structural characteristics, all rock minerals exhibit distinct spectral features in remote sensing images. To date, many scholars have conducted research on lithology extraction based on this principle, roughly categorized into lithology extraction based on image enhancement techniques and classification based on machine learning methods [3][4][5][6][7][8][9]. Image enhancement techniques commonly employ classic methods such as Landsat, ASTER, and Sentinel-2 data, combined with techniques like principal component analysis (PCA), band ratio (BR), matched filtering (MF), and color composite (CC) [10][11][12][13]. ...
Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang’s Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.
... The study conducted by wang and tian (2021) [70], focuses on the use of a backpropagation neural network (BPNN) to extract and classify lithological information in hyperspectral remote sensing data, specifically targeting rocks as the research object. The study utilizes normalized hyperspectral image data to extract lithological spectral and spatial information, constructing a deep learning-based model for classification. ...
ithological mapping is a fundamental undertaking in geological research, mineral resource exploration, and environmental management. However, conventional methods for lithological mapping are often laborious and challenging, particularly in remote or inaccessible areas. Fortunately, a transformative solution has emerged through the integration of remote sensing and machine learning algorithms, providing an efficient and accurate means of deciphering the geological features of the Earth’s crust. Remote sensing offers vast and comprehensive data across extensive geographical regions, while machine learning algorithms excel at recognizing intricate patterns and features in the data, enabling the classification of different lithological units. Compared to traditional methods, this approach is faster, more efficient, and remarkably accu�rate. The combination of remote sensing and machine learning presents numerous advantages, including the ability to amalgamate multiple data sources, provide up-to-date information on rapidly changing regions, and manage vast volumes of data. This review article delves into the invaluable contributions of remote sensing and machine learning algorithms to lithological mapping. It extensively explores diverse remote sensing datasets, such as Landsat, Sentinel-2, ASTER, and Hyperion data, which can be effectively harnessed for this purpose. Additionally, the study investigates a range of machine learning algorithms, including SVM, RF, and ANN, specifically tailored for lithological mapping. By scrutinizing practical use cases, the review underscores the strengths, limitations, and potential future developments of remote sensing and machine learning algorithms in the context of lithological mapping. Practical use cases have demonstrated the immense potential of machine learning algorithms, with the SVM classifier emerging as a reliable and accurate option for lithological mapping. Moreover, the choice of the most appropriate data source depends on the specific objectives of the application. Overall, the transformative potential of remote sensing and machine learning in lithological mapping cannot be overstated. This integrated approach not only enhances our understanding of geological features but also enables diverse applications in geological research and environmental management. With the promise of a more informed and sustainable future, the utilization of remote sensing and machine learning in lithological mapping represents a pivotal advancement in the field of geological sciences.
... Hyperspectral remote sensing lithological classification techniques infer the rock properties through the spectral characteristics of rocks, determine the lithology types and spatial distribution of the surface, and combine the classification results into a geological map and output expression. Compared with traditional lithology classification methods, remote sensing lithology classification has technical advantages, such as macro-scale capability, rapid processing, and non-pollution, and thus is often used as the main means of geological exploration for large-area remote sensing geological survey and thematic mapping [8][9][10][11]. It is necessary to develop high-precision geological and mineral exploration by investigating the application potential of hyperspectral remote sensing technologies in large-scale lithology classification and mineral resource exploration, which is conducive to realizing the deep integration of hyperspectral remote sensing technology, geological survey, and green exploration. ...
Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.
... In the field of disaster for example, remote sensing approaches are used for monitoring meteorological disasters (Ye, 2022) and geology (Z. Wang & Tian, 2021). Some specific studies on meteorological disasters are studies on floods (Tariq & Van de Giesen, 2012). ...
This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.
... One popular set of tools is used for statistical endmember selection, including approaches such as the pixel purity index [39], N-finder [40], and vertex component analysis [41]. Another set is used for discrete image classification, including both traditional algorithms like maximum likelihood (e.g., [42]) and support vector machines [43], as well as more recent neural network-based approaches [44,45]. Such discrete classification approaches almost entirely operate using various statistical operators to partition the variance-based spectral feature space. ...
We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary to, but distinct from, maps of mineral abundance. The approach synthesizes two concepts in imaging spectroscopy data analysis: the spectral mixture residual and joint characterization. First, the mixture residual uses a linear, generalizable, and physically based continuum removal model to mitigate the confounding effects of terrain and vegetation. Then, joint characterization distinguishes spectrally distinct geologic units by isolating residual, absorption-driven spectral features as nonlinear manifolds. Compared to most traditional classifiers, important strengths of this approach include physical basis, transparency, and near-uniqueness of result. Field validation confirms that this approach can identify regions of interest that contribute significant complementary information to PCA alone when attempting to accurately map spatial boundaries between lithologic units. For a geologist, this new type of base map can complement existing algorithms in exploiting the coming availability of global hyperspectral data for pre-field reconnaissance and geologic unit delineation.
