Piecewise anomaly detection using minimal learning machine for hyperspectral images
Raita-Hakola, A.-M., & Pölönen, I. (2021). Piecewise anomaly detection using minimal learning machine for hyperspectral images. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, E. Honkavaara, M. Scaioni, J. Zhang, A. Peled, L. Wu, R. Li, M. Yoshimura, K. Di, O. Altan, H. M. Abdulmuttalib, & F. S. Faruque (Eds.), XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III (V-3-2021, pp. 89-96). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprs-annals-V-3-2021-89-2021
Julkaistu sarjassa
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesPäivämäärä
2021Tekijänoikeudet
© Author(s) 2021
Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.
...
Julkaisija
Copernicus PublicationsKonferenssi
International Society for Photogrammetry and Remote Sensing CongressKuuluu julkaisuun
XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission IIIISSN Hae Julkaisufoorumista
2194-9042Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/100930894
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SALisätietoja rahoituksesta
This study is partly funded by the Academy of Finland (Grant No. 327862).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Minimal learning machine in anomaly detection from hyperspectral images
Pölönen, Ilkka; Riihiaho, Kimmo; Hakola, Anna-Maria; Annala, Leevi (International Society for Photogrammetry and Remote Sensing, 2020)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 ... -
A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders
Penttilä, Jeremias (2017)Menetelmä poikkeavuuksien havaitsemiseen hyperspektrikuvista käyttäen syviä konvolutiivisia autoenkoodereita. Poikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan ... -
Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions
Paoli, John; Pölönen, Ilkka; Salmivuori, Mari; Räsänen, Janne; Zaar, Oscar; Polesie, Sam; Koskenmies, Sari; Pitkänen, Sari; Övermark, Meri; Isoherranen, Kirsi; Juteau, Susanna; Ranki, Annamari; Grönroos, Mari; Neittaanmäki, Noora (Medical Journals Sweden AB, 2022)Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral ... -
Minimal learning machine in hyperspectral imaging classification
Hakola, Anna-Maria; Pölönen, Ilkka (SPIE, 2020)A hyperspectral (HS) image is typically a stack of frames, where each frame represents the intensity of a different wavelength of light. Each spatial pixel has a spectrum. In the classification of the HS image, each spectrum ... -
Updating strategies for distance based classification model with recursive least squares
Raita-Hakola, Anna-Maria; Pölönen, Ilkka (Copernicus Publications, 2022)The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.