SAR IMAGE PROCESSING FOR CROP MONITORING

Transcription

1 SAR IMAGE PROCESSING FOR CROP MONITORING Anne Orban, Dominique Derauw, and Christian Barbier Centre Spatial de Liège Université de Liège Agriculture and Vegetation at a Local Scale Habay-La-Neuve, 20 September 2005

2 CONTENTS I. CONTEXT To present advanced SAR processing techniques potentially applicable to crop monitoring : II. II.1 II.2 InSAR Basic Principles Sample Results III. PolSAR III.1 Basic Principles III.2 Sample Results IV. PolInSAR IV.1 Basic Principles IV.2 A Picture Book Example V. CONCLUSIONS AND PERSPECTIVES

3 I. CONTEXT r pixel = { A exp( j { φ ) { p amplitude SAR phase 123 polarisation state InSAR PolSAR PolInSAR

4 II. InSAR Projects : TELSAT Project T3/12/012 «Demonstration and Evaluation of SAR Interferometry» ( ) CSL ERS Tandem Project B302 «An Assessment of SAR Phasimetry by Case Studies in Tectonics and Agronomy» ( ) CSL, FUSAGx, CRA, UCL-MILA, ULg-LGT STEREO Project SR/00/01 «Modelling Crop Growth Based on Hydrology and Assimilation of Remotely Sensed Data» ( ) UCL, UGent, CSL

5 II.1 InSAR : Basic Principles Pass 1 (µ 1 ) Pass 2 (µ 2 ) (µ 1 µ 2 *) Interference pattern ==> Topography, terrain and atmospheric changes

6 II.2 InSAR : Sample Results Proc. FRINGE 99 Symp., Liège Nov. 1999, ESA SP-478

7 Basic Product : the Coherence Map γ = = γ µ SNR 1 γ µ 1 * µ 1 * µ 2 µ 2 Baseline γ * µ 2 Tempora (ERS acquisition over Belgium: region of Charleroi - 03 & 04/1996 Interfererometric baseline = 330 meters)

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10 III. PolSAR Projects : Project SA/12/001 «Development of a SAOCOM SAR Processor» ( ) CSL, SPACEBEL STEREO Project SR/00/01 «Modelling Crop Growth Based on Hydrology and Assimilation of Remotely Sensed Data» ( ) UCL, UGent, CSL

11 III.1 PolSAR : Basic Principles Single-polarization mode transmit in 1 single linear polarization: H or V receive in the same polarization 1 acquisition: HH or VV Dual-polarization mode transmit in H or V receive in H and V 2 acquisitions: HH/HV or VV/HV or HH/VV Quad-polarization mode transmit alternatively H and V receive in H and V 4 acquisitions: HH HV VH VV

12 PolSAR provides scattering mechanisms information. H. k r V µ r U int r r = µ.e Vegetation cover Soil

13 III.2 PolSAR : Sample Results Polarimetric Processor Input: images produced by the SAR processor Backscattering coefficient images σ Quad-polarimetric processor Dual-polarimetric processor

14 1.Backscattering Coefficient Images σ VV σ HH σ HV All the images presented here have been produced at CSL, using polarimetric data provided by the DLR

15 2. Quad-Polarimetric Processor Shh Svh Shv Svv Calibration Co-registration Scattering matrix S: Shh Shv Svh Svv Stokes matrix M (real, symmetric) Coherency vector/coherency matrix Transmitted and received polarization angles: (chi-t, psi-t), (chi-r, psi-r) Polarization synthesis/ Polarization signatures: sig [chi(t),psi(t),chi(r),psi(r)] Eigenvalues/eigenvectors decomposition Entropy/Anisotropy

16 Decomposition H/A/α H α A 3 H = P i log 3 ( P ) i= 1 P i i = λ i λ i α target scattering type [0,90 ] Classification methods based on scattering mechanisms: λ2 λ3 A = λ + λ urban areas, forests, vegetated/non-vegetated, clear-cut, water/ice surfaces 2 3

17 3. Dual-Polarimetric Processing 2 measurements: HH/HV or VV/HV no polarization synthesis no decomposition algorithm Channel 1 SLC image Channel 2 SLC image - coherence analysis - interferogram PRI images generation SLC images calibration PolSAR processing PRI images calibration Backscattered coefficient σ (ch1) Inter-channel correlation measurement Interferogram channel 1/channel 2 Backscattered coefficient σ (ch2) Coherence map γ Phase map φ

18 Coherence HH/VV Interferogram HH/VV

19 IV. PolInSAR Project : STEREO Project SR/00/53 «Polarimetric SAR Interferometry» ( ) CSL, UCL, RMA

20 IV.1 PolInSAR : Basic Principles PolInSAR = vector InSAR InSAR height information PolSAR scatteing mechanisms information PolInSAR height distribution of scattering mechanisms

21 IV.2 PolInSAR : A Picture Book Example S.R. Cloude and K.P. Papathanasiou, Polarimetric SAR Interferometry, IEEE Trans. Geosci. Remote Sensing 36(5), (Sept. 1998)

22 The Coherence Maps

23 Decomposition Into Coherence-Optimized States

24 Interpretation through a MODEL

25 V. CONCLUSIONS AND PERSPECTIVES 1) ALL processing tools are now developed and available at CSL 2) Need of DATA - Reliable acquisition plans - Future sensors (RADARSAT-2, TerraSAR X, ALOS, SAOCOM, )