MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel dista...

Full description

Bibliographic Details
Main Authors:	I. Pölönen, K. Riihiaho, A.-M. Hakola, L. Annala
Format:	Article
Language:	English
Published:	Copernicus Publications 2020-08-01
Series:	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:	https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdf

Similar Items

PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES
by: A.-M. Raita-Hakola, et al.
Published: (2021-06-01)

A Do-It-Yourself Hyperspectral Imager Brought to Practice with Open-Source Python
by: Kimmo Aukusti Riihiaho, et al.
Published: (2021-02-01)

PRACTICAL APPROACH FOR HYPERSPECTRAL IMAGE PROCESSING IN PYTHON
by: L. Annala, et al.
Published: (2018-04-01)

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
by: Leevi Annala, et al.
Published: (2020-10-01)

anomaly detection for hyperspectral imagery
by: Hsien-Ting Chen, et al.
Published: (2006)

HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS
by: J. G. Rejas Ayuga, et al.
Published: (2016-06-01)

Anomaly detection and compensation for hyperspectral imagery
by: Cho, Choongyeun, 1973-
Published: (2006)

Anomaly Detection from Hyperspectral Remote Sensing Imagery
by: Qiandong Guo, et al.
Published: (2016-12-01)

Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images
by: Paz Abel, et al.
Published: (2010-01-01)

Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images
by: Antonio Plaza, et al.
Published: (2010-01-01)

Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary
by: Yubin Niu, et al.
Published: (2016-03-01)

Hyperspectral Anomaly Detection With Morphological Random Walker
by: Zhihong Huang, et al.
Published: (2021-01-01)

A Semiparametric Model for Hyperspectral Anomaly Detection
by: Dalton Rosario
Published: (2012-01-01)

Structured Background Modeling for Hyperspectral Anomaly Detection
by: Fei Li, et al.
Published: (2018-09-01)

Hyperspectral Anomaly Change Detection Based on Autoencoder
by: Meiqi Hu, et al.
Published: (2021-01-01)

FrHPI: A Discriminative Patch-Image Model for Hyperspectral Anomaly Detection
by: Hao Li, et al.
Published: (2021-01-01)

DETECTION OF METHANE PLUMES IN HYPERSPECTRAL IMAGES FROM SENTINEL-5P BY COUPLING ANOMALY DETECTION AND PATTERN RECOGNITION
by: E. Ouerghi, et al.
Published: (2021-06-01)

Machine learning to detect anomalies in datacenter
by: Lindh, Filip
Published: (2019)

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm
by: Yuan Yuan, et al.
Published: (2019-01-01)

Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines
by: Shangzhen Song, et al.
Published: (2020-01-01)

Orthogonal Subspace Projection Target Detector for Hyperspectral Anomaly Detection
by: Chein-I Chang, et al.
Published: (2021-01-01)

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning
by: Yanjie Li, et al.
Published: (2021-01-01)

Subspace analysis isolation forest for hyperspectral anomaly detection
by: HUANG Yuancheng, et al.
Published: (2021-03-01)

Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
by: Chensong Yin, et al.
Published: (2021-12-01)

Hyperspectral Anomaly Detection With Otsu-Based Isolation Forest
by: Yuxiang Zhang, et al.
Published: (2021-01-01)

Hyperspectral Anomaly Detection Via Dual Collaborative Representation
by: Guoyun Zhang, et al.
Published: (2020-01-01)

Adaptive multi-layer structure with spatial-spectrum combination for hyperspectral image anomaly detection
Published: (2021-06-01)

Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
by: Mohsen Zare-Baghbidi, et al.
Published: (2015-01-01)

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation
by: Dandan Ma, et al.
Published: (2018-05-01)

Machine learning for time series anomaly detection
by: Tinawi, Ihssan.
Published: (2019)

Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity
by: Dirk Borghys, et al.
Published: (2012-01-01)

Hyperspectral Anomaly Detection via Spatial Density Background Purification
by: Bing Tu, et al.
Published: (2019-11-01)

Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery
by: Wenzheng Wang, et al.
Published: (2018-10-01)

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade
by: Dandan Ma, et al.
Published: (2019-10-01)

Atmospheric correction and anomaly detection of remotely sensed hyperspectral imagery
by: Young, Andrew Thomas
Published: (2017)

Hyperspectral Anomaly Detection Method Based on Adaptive Background Extraction
by: Min Li, et al.
Published: (2020-01-01)

Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
by: Ivan Racetin, et al.
Published: (2021-05-01)

Image processing and machine learning‐based classification method for hyperspectral images
by: Orhan Yaman, et al.
Published: (2021-02-01)

Anomaly Detection for Hyperspectral Images Based on Anisotropic Spatial-Spectral Total Variation and Sparse Constraint
by: Huihui Ju, et al.
Published: (2018-01-01)

Study on Fruit Brix by Hyperspectral Detection Based on Machine Learning
by: WU, BO-HUEI, et al.
Published: (2019)