Land Cover Feature recognition by fusion of PolSAR, PolInSAR and optical data

Transcription

1 Land Cover Feature recognition by fusion of PolSAR, PolInSAR and optical data Shimoni, M., Borghys, D., Heremans, R., Milisavljević, N., Pernel, C. Derauw, D., Orban, A. PolInSAR Conference, ESRIN, January 2007

2 RESEARCH GOALS The main research goal is to fuse different frequency E-SAR PolSAR data as well as PolInSAR with Daedalus optical data for land cover classification and land cover feature recognition. This research also assigns the following target assessments: Do fused features from different SAR frequencies are complementary and adequate for land cover classification; Do PolInSAR features are complementary to the PolSAR information and essential for producing accurate classification of different land cover types as man-made object, water bodies, forest, crops and bare soils; Do optical data are complementary information for the SAR data and are necessary for the production of accurate land-cover classification. DATA SET Test site : Glinska Poljana, Croatia. Date : 6 to 10 August 2001; SAR: E-SAR L-band, P-band full polarimetric and dual-pass interferometry data set, resolution: 2 m (L) and 4 m (P); Optical: Daedalus 10 bands µm, resolution: 1 m; Excessive ground truth campaign

3 DERIVED FEATURE SETS PolSAR PolInSAR Optical PolSAR coherences Pauli decomposition Krogager decomposition Optimal coherences Mean magnitude Eigenvalue λ 1 Daedalus bands Freeman decomposition Huynen decomposition Barnes decomposition Eigenvalue λ 2 Stdv of the magnitude γ Stdv of the phase φ PCA234 H/A/α decomposition Asymetry Holm decomposition Neumann decomposition Lee Classifier A 1 Lee Classifier A 2 Directional filter 25 L-band PolSAR 25 P-band PolSAR 13 L-band PolinSAR 13 P-band PolinSAR 26 Optical

4 Feature based fusion 19 classifications High decision level fusion

5 FUSION METHODS Feature level fusion using Logistic Regression (LR): Finds an optimal combination of channels for detecting a given class, based on the learning set: N p x, y r ( TgtClass C) exp β0 + i = 1+ exp β0 + = 1 N Ci ( x, y) βi Ci ( x, y) βi 1 Implicit channel selection by using step-wise optimisation method for finding β i s. FUZZY based decision fusion: The fuzzy set theory allows an object to have partial membership in more than one set: A = {( x, µ A( x)) x X} µ A (x) is the grade of membership of x in A which maps X to the membership space M. For each class, we combine the classification results using maximum rule. i=

6 RESULTS

7 CONCLUSIONS L-band HH SLC scene Pauli decomposition LR classification results Fuzzy classification results For both fusion methods the overall accuracy for each of the fused sets is better than the accuracy for the separate sets of features; Fused features from different SAR frequencies are complementary and adequate for land cover classification; PolInSAR features are complementary to the PolSAR information and essential for producing accurate classification of different land cover types as man-made object, water bodies, forest, crops and bare soils; The optical data is complementary information for the SAR data but not necessary for the production of accurate land-cover classification; The overall fusion performance of the fuzzy-based approach is slightly better than the feature fusion by logistic regression for most of the combinations of feature sets.