MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Remote Sensing Laboratory Dept. of Information and Communication Technology University of Trento Via Sommarive, 15, Trento, Italy Francesca BOVOLO and Lorenzo BRUZZONE Web pages at: A Wavelet-Based Change-Detection Technique for Multitemporal SAR Images

2 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Outline Introduction and background. Aim of the work. Proposed wavelet-based change-detection approach: Multiscale decomposition; Adaptive scale-selection; Automatic multiscale fusion. Experimental results. Conclusions and future developments.

3 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Introduction Unsupervised change-detection in multitemporal remote-sensing images plays an important role in several application domains (the ground truth on the analyzed problem is not available in many real applications). Unlike optical multispectral images, Synthetic Aperture Radar (SAR) data are less used in unsupervised change-detection problems. This is due to: the complexity of SAR data pre-processing; the presence of multiplicative speckle noise that renders difficult the separation between changed and unchanged classes. However, the use of SAR images for change detection is particularly attractive from the operational view point as they are not affected by sun light and atmospheric conditions.

4 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Aim of the Work Develop a novel wavelet-based multiscale approach to unsupervised change detection in multitemporal SAR images (suitable for single- channel single-polarization multitemporal SAR data); Adaptively exploiting information at different scales in order to obtain change-detection maps that shows: high accuracy in both homogeneous and border regions; high geometric fidelity.

5 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Proposed Approach: Block Scheme Scale-Driven Fusion X1X1 t 2 SAR image t 1 SAR image Adaptive Scale Identification Image Comparison (log-ratio) “Log-ratio” Image Multiscale Decomposition Change-detection map Ω ={ω c, ω u } X2X2 X LR M

6 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Image Comparison Difference operator: the distribution depends on both the relative change and the reference intensity level in the original images. Thus changes are not detected in the same way in high and low intensity regions. Ratio operator: reduces the multiplicative distortion affects common to the two considered images due to speckle noise. The distribution depends only on the relative changes between images. Equivalent number of look of the SAR data Intensity of SAR images at t 1 and t 2 Usually log-ratio operator is used instead of the ratio as the log-ratio image has a more symmetrical statistical distribution and transforms the residual multiplicative noise model into an additive noise model.

7 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Multiscale decomposition (1) Objective: compute a multiscale sequence of log-ratio images characterized by different trade-offs between SNR and geometrical-detail content. l(.)       h(.) l(.) h(.) l(.) Column wiseRow wise Proposed approach: apply iteratively the two-dimensional wavelet transform to the log-ratio image following the Mallat pyramidal algorithm.

8 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Multiscale decomposition (2) The desired multiresolution sequence is computed applying inverse wavelet transform at each resolution level independently: LL 1 LH 1 HL 1 HH 1 Level 1 LL 2 LH 2 HL 2 HH 2 Level 2 LL N-1 LH N-1 HL N-1 HH N-1 Level N-1 to all wavelet coefficient after thresholding detail sub-bands; only to the approximation sub- bands (neglecting detail sub-bands).

9 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Adaptive Scale Identification Definition: a resolution level is reliable for a given pixel if the pixel does not belong to a border area at that level. The set of n reliable scales for the generic pixel ( i,j ), is made up of all the sequential resolution levels that satisfy the following condition: Homogeneous regions S ij = N-1 Border regions S ij = 0 “Intermediate” regions Level 0 < S ij < N -1 S ij is the optimal scale: the lowest resolution level that satisfies the definition of reliable scale for a given pixel. CV: is the coefficient of variation (normalized standard deviation) computed over the whole image; LCV: is the local coefficient of variation computed over a moving window;

10 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Scale-Driven Fusion Three different scale-driven fusion strategies have been considered for generating the change-detection map: Optimal scale selection (OSS); Fusion at decision level (FDL); Fusion at feature level (FFL). Optimal scale selection: and Pixel label at optimal resolution level S ij Fusion at decision level: # of times the pixel ( i,j ) is assigned to class  k over the set of its reliable scales Pixel label in the final change-detection map

11 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Fusion at feature level: Compute a new set of N images by averaging all possible sequential combinations of reliable scales:, with For each pixel compute the final label as: Scale-dependent threshold value Scale-Driven Fusion

12 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Data Set Description Study area: a forest area in the central Canada (Saskatchewan). Multitemporal data set: a portion of 350×350 pixels two images acquired by the SAR sensor of ERS-1 the 1 st July and the 14 th October Objective: identify forest fires that affected the considered area between the two acquisition dates. July 1995October 1995Reference Map

13 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Multiresolution Decompositon Level 4 Level 5 Level 6 Level 1 Level 2 Level 3 Level 0 (Log-ratio image)

14 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Experimental Results Proposed approach Strategy False Alarms Missed AlarmsOverall Errors OSS FDL FFL Classical algorithms StrategyFalse AlarmsMissed AlarmsOverall Errors Wavelet de-noising Lee de-noising

15 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Experimental Results Classical approach (de-noising with the Lee filter) Proposed approach (FFL strategy) Change-detection maps Reference map

16 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Conclusions A novel adaptive wavelet-based technique for change-detection in multitemporal SAR images has been proposed. Two novel methodological contributions characterize the proposed method compared with traditional algorithms: automatic and adaptive selection of the reliable scales to be used in change detection for each pixel; scale-driven fusion strategies; The presented technique shows both high sensitivity to geometrical details and a high robustness to noisy speckle components in homogeneous areas; Experimental results obtained on real multitemporal SAR data confirm the effectiveness of the proposed approach and in particular of the FFL strategy.

17 MULTITEMP 2005 – Biloxi, Mississippi, USA, May 16-18, 2005 Future Developments Explore different strategies for performing the multiresolution decomposition step in order to proper modeling the change information at different resolution levels (e.g., stationary wavelet transform). Integrate automatic thresholding procedures in the scale-dependent fusion procedure. Extend the use of the proposed approach to change detection in very high resolution SAR images and multiband and fully polarimetric SAR data.