Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Autor: Sanja Scepanovic, Oleg Antropov, Pekka Laurila, Yrjo Rauste, Vladimir Ignatenko, Jaan Praks

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 10357-10374 (2021)

Land cover (LC) mapping is essential for monitoring the environment and understanding the effects of human activities on it. Recent studies demonstrated successful applications of specific deep learning models to small-scale LC mapping tasks (e.g., w

Externí odkaz: https://doaj.org/article/779aa9b48910447088b494dc772ba03e

Experimental Demonstration of a Novel End-to-End SAR Range Ambiguity Suppression Method

Autor: Ozan Dogan, Vladimir Ignatenko, Darren Muff, Leszek Lamentowski, Matthew Nottingham, Andrea Radius, Pierre Leprovost, Tino Seilonen

Publikováno v: 2022 IEEE Radar Conference (RadarConf22).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1bf0afffc88cc7f9553d4f116248495a
https://doi.org/10.1109/radarconf2248738.2022.9764267

ICEYE Microsatellite SAR Constellation Status Update: Long Dwell Spotlight and Wide Swath Imaging Modes

Autor: Darren Muff, Leszek Lamentowski, Andrea Radius, Vladimir Ignatenko, Matthew Nottingham

Publikováno v: IGARSS

The ICEYE constellation features the first operational mi-crosatellite based X-band SAR sensors suitable for all-weather day-and-night Earth Observation. ICEYE microsatellites feature an active phased array antenna with beam steering capabilities in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1d505353752dbfc8663d66bd6b055735
https://doi.org/10.1109/igarss47720.2021.9554486

Classification of Wide-Area SAR Mosaics:Deep Learning Approach for Corine Based Mapping of Finland Using Multitemporal Sentinel-1 Data

Autor: Oleg Antropov, Yrjö Rauste, Jaan Praks, Vladimir Ignatenko, Sanja Sepanovic, Anne Lönnqvist

Publikováno v: Antropov, O, Rauste, Y, Šćepanović, S, Ignatenko, V, Lönnqvist, A & Praks, J 2020, Classification of Wide-Area SAR Mosaics : Deep Learning Approach for Corine Based Mapping of Finland Using Multitemporal Sentinel-1 Data . in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 : Proceedings ., 9323855, IEEE Institute of Electrical and Electronic Engineers, IEEE International Geoscience and Remote Sensing Symposium Proceedings, pp. 4283-4286, IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Waikoloa, Hawaii, United States, 26/09/20 . https://doi.org/10.1109/IGARSS39084.2020.9323855
IGARSS

Here, we examine a deep learning approach to perform land cover classification using country-wide SAR mosaics compiled using multitemporal Sentinel-1 imagery. We capitalize on our earlier study [1], demonstrating the suitability of deep learning mode

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68d9d6760979fdabd03b31c0c2ea6e5a
https://cris.vtt.fi/en/publications/29d9187f-88c8-4836-bc03-10e13e9f85fb

Assessment of Operational Microsatellite Based SAR for Earth Observation Applications

Autor: Rafal Modrzewski, Miska Kauppinen, Jaan Praks, Vladimir Ignatenko, Pekka Laurila, Oleg Antropov

Publikováno v: Antropov, O, Praks, J, Kauppinen, M, Laurila, P, Ignatenko, V & Modrzewski, R 2018, Assessment of Operational Microsatellite Based SAR for Earth Observation Applications . in 2018 2nd URSI Atlantic Radio Science Meeting, AT-RASC 2018 ., 8471324, IEEE Institute of Electrical and Electronic Engineers, 2nd URSI Atlantic Radio Science Meeting, AT-RASC 2018, Gran Canaria, Spain, 28/05/18 . https://doi.org/10.23919/URSI-AT-RASC.2018.8471324

Space assets have become more affordable due to miniaturization of sensor and satellite platform technology, which allows significant reduction of launch and development costs. The first wave of new, radically smaller EO (Earth Observation) satellite

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c70884b6a66411bfbd2e8ecd4861c1c
https://doi.org/10.23919/ursi-at-rasc.2018.8471324