Minimal learning machine in anomaly detection from hyperspectral images
Pölönen, I., Riihiaho, K., Hakola, A.-M., & Annala, L. (2020). Minimal learning machine in anomaly detection from hyperspectral images. In N. Paparoditis, C. Mallet, F. Lafarge, 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, Commission III (pp. 467-472). International Society for Photogrammetry and Remote Sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-467-2020
Julkaistu sarjassa
International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesPäivämäärä
2020Tekijänoikeudet
© Authors 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 for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.
Julkaisija
International Society for Photogrammetry and Remote SensingKonferenssi
ISPRS CongressKuuluu julkaisuun
XXIV ISPRS Congress, Commission IIIISSN Hae Julkaisufoorumista
1682-1750Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/41834791
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiahanke, SALisätietoja rahoituksesta
This study is partly funded by Academy of Finland (Grant 327862).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Piecewise anomaly detection using minimal learning machine for hyperspectral images
Raita-Hakola, A.-M.; Pölönen, I. (Copernicus Publications, 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 ... -
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 ... -
Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks
Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include ... -
Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity
Tuominen, Sakari; Näsi, Roope; Honkavaara, Eija; Balazs, Andras; Hakala, Teemu; Viljanen, Niko; Pölönen, Ilkka; Saari, Heikki; Ojanen, Harri (MDPI, 2018)Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to ... -
Editorial for the special issue "Frontiers in spectral imaging and 3D technologies for geospatial solutions"
Honkavaara, Eija; Karantzalos, Konstantinos; Liang, Xinlian; Nocerino, Erica; Pölönen, Ilkka; Rönnholm, Petri (MDPI, 2019)This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.