The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon color, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone-camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colors to test this algorithm. The algorithm robustly recognized previously-observed units by their color, while requiring only a single image or a few images to learn colors as familiar, demonstrating its fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International Journal of Astrobiolog

Application of remote sensing to state and regional problems

The methods and procedures used, accomplishments, current status, and future plans are discussed for each of the following applications of LANDSAT in Mississippi: (1) land use planning in Lowndes County; (2) strip mine inventory and reclamation; (3) white-tailed deer habitat evaluation; (4) remote sensing data analysis support systems; (5) discrimination of unique forest habitats in potential lignite areas; (6) changes in gravel operations; and (7) determining freshwater wetlands for inventory and monitoring. The documentation of all existing software and the integration of the image analysis and data base software into a single package are now considered very high priority items

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Exploring issues of balanced versus imbalanced samples in mapping grass community in the telperion reserve using high resolution images and selected machine learning algorithms

ABSTRACT Accurate vegetation mapping is essential for a number of reasons, one of which is for conservation purposes. The main objective of this research was to map different grass communities in the game reserve using RapidEye and Sentinel-2 MSI images and machine learning classifiers [support vector machine (SVM) and Random forest (RF)] to test the impacts of balanced and imbalance training data on the performance and the accuracy of Support Vector Machine and Random forest in mapping the grass communities and test the sensitivities of pixel resolution to balanced and imbalance training data in image classification. The imbalanced and balanced data sets were obtained through field data collection. The results show RF and SVM are producing a high overall accuracy for Sentinel-2 imagery for both the balanced and imbalanced data set. The RF classifier has yielded an overall accuracy of 79.45% and kappa of 74.38% and an overall accuracy of 76.19% and kappa of 73.21% using imbalanced and balanced training data respectively. The SVM classifier yielded an overall accuracy of 82.54% and kappa of 80.36% and an overall accuracy of 82.21% and a kappa of 78.33% using imbalanced and balanced training data respectively. For the RapidEye imagery, RF and SVM algorithm produced overall accuracy affected by a balanced data set leading to reduced accuracy. The RF algorithm had an overall accuracy that dropped by 6% (from 63.24% to 57.94%) while the SVM dropped by 7% (from 57.31% to 50.79%). The results thereby show that the imbalanced data set is a better option when looking at the image classification of vegetation species than the balanced data set. The study recommends the implementation of ways of handling misclassification among the different grass species to improve classification for future research. Further research can be carried out on other types of high resolution multispectral imagery using different advanced algorithms on different training size samples.EM201

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.http://sajg.geoscienceworld.orgam2021Geography, Geoinformatics and MeteorologyGeolog

A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.</jats:p

Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges