Denoising and Wavelet-Based Feature Extraction of MODIS Multi-Temporal Vegetation Signatures Lori Mann Bruce and Abhinav Mathur Electrical and Computer Engineering Department Mississippi State University

Outline Project Goals MODIS Data Denoising Methods Feature Extraction Methods Experimental Results Conclusions

MODIS Data For Invasives Detection Time NDVI Target Vegetation Alternate Vegetation

Noise in Spectral Signatures Encountering problems with Quality Assurance (QA) of MODIS imagery Hierarchical Data Format (HDF) – Self describing file format Science Data Sets (SDSs) – 2D, 3D or 4D arrays Attributes – text or other data that annotates the file (global) or arrays (SDSs) Metadata – ECS metadata for products (stored as attributes) .met file contains the ECS core metadata (includes QA information, date/time products acquired/produced, etc.) HDF-EOS Metadata - SWATH or GRID – (includes geometric information that relates data to specific earth locations)

MODIS images from January 2001 to December 2003 Click on the image Months: 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

MODIS images from January 2001 to December 2003 Time line EVI value

Denoising MODIS Time-Series Data moving average filter median filter 10 20 30 40 50 60 70 veg type1 10 20 30 40 50 60 70 10 20 30 40 50 60 70 10 20 30 40 50 60 70 veg type2 10 20 30 40 50 60 70 10 20 30 40 50 60 70

Feature Extraction from MODIS Time-Series Data 10 20 30 40 50 60 70 denoised veg type1 deniosed veg type2 Fourier Analysis 100 200 300 1 2 3 magnitude response phase response 100 200 300 -4 -2 2 4

Fourier-Based Feature Extraction 2.5 2 Magnitude 1.5 1 0.5 10 20 30 40 50 60 F2 F4 10 20 30 40 50 60 -2 2 Phase Frequency sample points

Wavelet Decompositions å = k j f x W ) ( , y Inverse DWT ) ( ), , x f W k j y = Discrete Wavelet Transform (DWT)

Wavelet-Based Feature Extraction Temporal Signature Haar Mother Wavelet Signal Approximation Approximation Coefficients Scale 2^3 Scale 2^2 Scale 2^1

Wavelet-Based Feature Extraction Mean F1, F2, …, F6

Fourier-Based Features Veg1 Noisy Veg2 Denoised Mean F1 2.63e05 2.79e0 5 2.78e05 Std F1 1.9e08 1.07e08 1.89e08 1.06e08 Mean F2 Std F2 Mean F3 1.26e05 1.23e05 1.20e05 Std F3 3.34e07 4.46e07 3.21e07 4.27e07 Mean F4 - 3 1 Std F4 9 10

Wavelet-Based Features Veg1 Noisy Veg2 Denoised Mean F1 834 1654 1339 1618 Std F1 109438 134644 111950 108233 Mean F2 3424 4662 2092 3820 Std F2 108333 143233 95250 195662 Mean F3 11001 10184 9151 8454 Std F3 578968 706544 423224 447922 Mean F4 15200 15251 14785 4612 Std F4 179830 92016 183090 74526 Mean F5 9902 11302 11641 12697 Std F5 784840 697503 671643 695616 Mean F6 3677 4159 5496 6193 Std F6 190165 102728 251508 118408

Classification Accuracies 67% 56% 75% Denoised – NN 81% 89% Denoised – ML Noisy – NN Noisy – ML Overall Veg2 Veg1 Fourier-Based Features 100% Denoised – NN Denoised – ML 95% 92% Noisy – NN Noisy – ML Overall Veg2 Veg1 Wavelet-Based Features

Conclusions MODIS time-series data has isolated noise spikes Fourier-based features less affected by noise than wavelet-based features Shape-preserving features needed for invasives detection project Wavelet-based features resulted in significantly higher accuracies than Fourier-based features Simple denoising methods (moving average or median filter) were sufficient

Questions Lori Mann Bruce, Ph.D. bruce@ece.msstate.edu

Feature Extraction from MODIS Time-Series Data 10 20 30 40 50 60 70 denoised veg type1 deniosed veg type2 Wavelet Analysis