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Use of Modified BM3D Filter and CNN Classifier for SAR Data to Improve Crop Classification Accuracy

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON)
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)978-1-7281-3882-4
ISBN (painettu)978-1-7281-3883-1
DOI - pysyväislinkit
TilaJulkaistu - 24 lokakuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaUkraine Conference on Electrical and Computer Engineering -
Kesto: 1 tammikuuta 2000 → …

Conference

ConferenceUkraine Conference on Electrical and Computer Engineering
LyhennettäUKRCON
Ajanjakso1/01/00 → …

Tiivistelmä

Monitoring of agricultural regions is an important task. Recent trends to solve it are based on applying multi-temporal remote sensing in order to obtain reliable crop classification maps. If a radar remote sensing is used, speckle presence in the original data reduces a classification accuracy. A negative impact of speckle can be reduced by image prefiltering procedure. Recent studies carried out for Sentinel-1 imagery have shown that more efficient pre-filtering usually results in better classification. Thus, here we propose a modification of block matching three-dimensional (BM3D) filter adapted to the properties of Sentinel-1 radar data. We demonstrate that its use leads to improved classification of crops in agricultural region of Ukraine compared to earlier methods which are based on other filters. Additionally, a convolutional neural network approach for crop mapping is investigated in this paper for decreasing noise impact and is compared with a traditional pixel-based multi-layer perceptron.

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