Integration of sar and optical dense time series for land cover monitoring

R. A. Molijn; L. Iannini; R. F. Hanssen; F. J. Van Leijen; R. Lamparelli; A. Coutinho

doi:10.1109/IGARSS.2017.8127960

Integration of sar and optical dense time series for land cover monitoring

R. A. Molijn, L. Iannini, R. F. Hanssen, F. J. Van Leijen, R. Lamparelli, A. Coutinho

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

Original language	English
Title of host publication	2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4330-4333
Number of pages	4
Volume	2017-July
ISBN (Electronic)	9781509049516
DOIs	https://doi.org/10.1109/IGARSS.2017.8127960
Publication status	Published - 1 Dec 2017
Event	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017: IEEE Geoscience and Remote Sensing - Fort Worth, United States Duration: 23 Jul 2017 → 28 Jul 2017 Conference number: 37 http://www.igarss2017.org/

Conference

Conference	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Abbreviated title	IGARSS 2017
Country/Territory	United States
City	Fort Worth
Period	23/07/17 → 28/07/17
Internet address	http://www.igarss2017.org/

Keywords

C-band SAR
Land cover mapping
Landsat
Sensor assimilation
Time series processing

Access to Document

10.1109/IGARSS.2017.8127960

Cite this

Molijn, R. A., Iannini, L., Hanssen, R. F., Van Leijen, F. J., Lamparelli, R., & Coutinho, A. (2017). Integration of sar and optical dense time series for land cover monitoring. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings (Vol. 2017-July, pp. 4330-4333). Article 8127960 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IGARSS.2017.8127960

@inproceedings{9a06b3bfc0764fcf8771bad481ec33ad,

title = "Integration of sar and optical dense time series for land cover monitoring",

abstract = "Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of S{\~a}o Paulo (Brazil), where recent ground surveying activities has been conducted.",

keywords = "C-band SAR, Land cover mapping, Landsat, Sensor assimilation, Time series processing",

author = "Molijn, {R. A.} and L. Iannini and Hanssen, {R. F.} and {Van Leijen}, {F. J.} and R. Lamparelli and A. Coutinho",

year = "2017",

month = dec,

day = "1",

doi = "10.1109/IGARSS.2017.8127960",

language = "English",

volume = "2017-July",

pages = "4330--4333",

booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 : IEEE Geoscience and Remote Sensing , IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",

url = "http://www.igarss2017.org/",

}

Molijn, RA, Iannini, L, Hanssen, RF , Van Leijen, FJ, Lamparelli, R & Coutinho, A 2017, Integration of sar and optical dense time series for land cover monitoring. in 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. vol. 2017-July, 8127960, Institute of Electrical and Electronics Engineers (IEEE), pp. 4330-4333, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8127960

Integration of sar and optical dense time series for land cover monitoring. / Molijn, R. A.; Iannini, L.; Hanssen, R. F. et al.
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Vol. 2017-July Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 4330-4333 8127960.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Integration of sar and optical dense time series for land cover monitoring

AU - Molijn, R. A.

AU - Iannini, L.

AU - Hanssen, R. F.

AU - Van Leijen, F. J.

AU - Lamparelli, R.

AU - Coutinho, A.

N1 - Conference code: 37

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

AB - Multi-temporal and multi-sensor solutions are essential to increase timeliness and reliability of land monitoring systems. This paper advocates the exploitation of the temporal contextual information provided by temporally dense SAR and optical data series series through the use of a Hidden Markov model (HMM)-based approach. An efficient strategy to incorporate the C-Band SAR data into the HMM framework, relying so far on Landsat, will be debated and assessed over a dynamic agricultural scenario, i.e. characterized by high temporal and spatial diversity in cropping practices. The site is located in the state of São Paulo (Brazil), where recent ground surveying activities has been conducted.

KW - C-band SAR

KW - Land cover mapping

KW - Landsat

KW - Sensor assimilation

KW - Time series processing

UR - http://www.scopus.com/inward/record.url?scp=85041798646&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2017.8127960

DO - 10.1109/IGARSS.2017.8127960

M3 - Conference contribution

AN - SCOPUS:85041798646

VL - 2017-July

SP - 4330

EP - 4333

BT - 2017 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

Y2 - 23 July 2017 through 28 July 2017

ER -

Molijn RA, Iannini L, Hanssen RF , Van Leijen FJ, Lamparelli R, Coutinho A. Integration of sar and optical dense time series for land cover monitoring. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Vol. 2017-July. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 4330-4333. 8127960 doi: 10.1109/IGARSS.2017.8127960

Integration of sar and optical dense time series for land cover monitoring

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this