Use of Modified BM3D Filter and CNN Classifier for SAR Data to Improve Crop Classification Accuracy
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON) |
| Kustantaja | IEEE |
| Sivumäärä | 6 |
| ISBN (elektroninen) | 978-1-7281-3882-4 |
| ISBN (painettu) | 978-1-7281-3883-1 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 24 lokakuuta 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | Ukraine Conference on Electrical and Computer Engineering - Kesto: 1 tammikuuta 2000 → … |
Conference
| Conference | Ukraine Conference on Electrical and Computer Engineering |
|---|---|
| Lyhennettä | UKRCON |
| Ajanjakso | 1/01/00 → … |
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.