Paper Reading AI Learner

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

2021-03-13 10:36:25
Hojat Shirmard, Ehsan Farahbakhsh, Dietmar Muller, Rohitash Chandra

Abstract

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 against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing data, and this area is now considered a hotspot in geoscience research. This paper reviews the implementation and adaptation of some popular and recently established machine learning methods for remote sensing data processing and investigates their applications for exploring different ore deposits. Lastly, the challenges and future directions in this critical interdisciplinary field are discussed.

Abstract (translated)

URL

https://arxiv.org/abs/2103.07678

PDF

https://arxiv.org/pdf/2103.07678.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot