Active Learning Methods for Remote Sensing Image Classification

By Devis Tuia, Frédéric Ratle, Fabio Pacifici, Mikhail F. Kanevski, William J. Emery. IEEE Transactions on Geoscience and Remote Sensing, 2009

513 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6c1dd5cd7468d05a936fa63877a035223884606a

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Citations in the corpus

Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, unknown): “classification [5], unmixing, target detection, etc.”
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, background): “In the literature, primary studies have devoted to classifying aerial images at pixel level, by assigning each pixel in an aerial image with a thematic class [43], [44].”
Exploring Models and Data for Remote Sensing Image Caption Generation (2017, background): “However, the studies of the remote sensing images are still concentrate on scene classification [5], [16], [17], [18], [19], object recognition [20], [21] and segmentation [22], [23], [24], [25].”
Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images (2011, methodology): “The first technique is MS by closest support vector (MS-cSV), which considers the smallest distance of the unlabeled samples to the n hyperplanes (associated to the n binary SVMs in a (OAA) multiclass architecture) as the uncertainty value.”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “To address the paucity of labels, novel learning frameworks such as semi-supervised learning, that leverages the structure in the unlabeled data for improving classification performance [54], and active learning, where an expert annotator is actively involved in the process of model building [55], h”
Monitoring Land-Cover Changes: A Machine-Learning Perspective (2016, background): “In a different line of research, active learning approaches have been devised to operate in land-cover classification scenarios with a paucity of labeled data [25], [26], [33]–[35], where the emphasis is on parsimoniously requesting training labels from an expert annotator during the process of lear”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “The growing applications of LULC mapping alongside the need for updating the existing maps have offered new opportunities to effectively develop innovative RS image classiﬁcation techniques in various land management domains to address local, regional, and global challenges [13]–[20].”
Active and Semisupervised Learning for the Classification of Remote Sensing Images (2014, methodology): “The use of AL for RS data classiﬁcation has been investigated in [8]–[11] and [30].”
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011, unknown): “- Pool of candidates U = {xi}l+ui=l+1 (U ∈ X , = 1).”
Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge (2015, methodology): “To achieve this purpose, there are three common types of strategies for intelligent sampling to select new labeled samples from a candidate pool of unlabeled samples [24]: (1) large-margin-based methods [32], which select candidates lying within the margin of the current support vector machine (SVM)”
Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data (2019, background): “The effectiveness of AL has been shown in research areas such as natural language processing [9, 10], computer vision [11, 12], and speech recognition [13, 14].”
Semisupervised Classification of Remote Sensing Images With Active Queries (2012, background): “Several strategies exist based on committees of models [25], [26], support vector machines (SVMs) [25], [27]–[29], and posterior probabilities in the prediction of the unlabeled samples [30]–[32].”