1. Impact of SAR data filtering on crop classification accuracy
UkrCon2017 1
 Impact of SAR data filtering
on crop classification accuracy
 M. Lavreniuk1, N. Kussul1, M. Meretsky1,
V. Lukin2, O. Rubel2, S. Abramov2
1Department of Space Information Technologies and Systems
Space Research Institute NAS Ukraine and SSA
Ukraine Kyiv, Ukraine
2Department of Transmitters, Receivers and Signal Processing, National Aerospace University, Kharkov, Ukraine

2. Radar remote sensing (image filtering)2
Synthetic aperture radar (SAR) is a tool employed for various applications of remote sensing as:
hydrology;
ecology;
forestry;
agriculture.
This is due to the following advantages of SAR as:
appropriate spatial resolution;
possibility to operate during day and night, in almost all weather conditions;
availability of SAR data;
periodicity of observations
Radar images can be used:
jointly with optical or infrared ones;
Separately if obtained for several frequencies and/or polarizations (see Sentinel images →);
Jointly if obtained in multi-temporal mode.

3. Radar remote sensing (speckle)3
Drawback:
Radar images acquired by SAR inevitably suffer from noise-like phenomenon called speckle.
Speckle is a specific kind of noise that has multiplicative nature and is often spatially correlated.
Speckle properties depend upon several factors including is this intensity or amplitude image, what is number of looks.
Noise-free, amplitude and intensity images (single look)

4. Radar remote sensing (speckle)4
Speckle presence in negative way influences all procedures of SAR image processing as:
segmentation;
lossless compression;
classification;
object and edge detection;
texture detection and feature determination.
Denoising or despeckling is used to improve quality of SAR RS images and improve performance of subsequent application (classification, compression, etc.):
Denoising efficiency can be characterized in different ways.
This can be done using conventional criteria as output MSE or PSNR.
Meanwhile, filtering efficiency can be characterized indirectly, via criteria that describe classification accuracy.
Our goals:
- to analyze filtering efficiency by determining its impact on crop classification;
to study such properties of Sentinel SAR images that are important for filtering;
to compare performance of different filters.

5. 8x8 normalized DCT spectra for several images
Sentinel SAR images
(main properties) 6
It has been established that speckle is pure multiplicative.
Its relative variance is about 0.05.
Cross correlation of VV and VH components is about 0,85.
Speckle is spatially correlated.
Spatial correlation properties are practically the same for all considered image fragments and for both polarization components.
8x8 normalized DCT spectra for several images
Notes: small indices correspond to low spatial frequencies;
Estimates have been obtained in homogeneous image fragments using robust techniques.
Analysis clearly shows that speckle is spatially correlated.

6. Requirements to despeckling
and analyzed filters 7
Main requirements to filters:
to suppress speckle;
to preserve edges and fine details;
to preserve texture;
to preserve mean in homogeneous regions and ratios for multichannel data.
Analyzed filters:
Filters available in ESA SNAP Toolbox
The DCT-based filters and, particularly, the conventional DCT-based filter that have demonstrated high efficiency for a wide class of images and noise models. There are modifications for pure multiplicative noise. Filtering is fast enough.
Images corrupted by multiplicative and other types of signal dependent noises can be processed applying homomorphic and/or variance stabilizing transformations to produce additive noise. Then, they can be processed by standard DCT-based filter,
A block matching 3D (BM3D) filter is considered to be the most effective in AWGN suppression for component (grayscale) images where this filter also exploits DCT-based denoising as a basic mechanism of noise removal. Homomorphic transform is needed.

7. Classification approach11
4. Geospatial analysis (data fusion from the classification maps and vector information)
3. Map filtration (voting and weighted voting approaches with division parcels into the fields)
2. Universal deep learning approach for time series classification at the regional level
1. No-data pixels restoration (clouds and shadows) using self-organized Kohonen maps

8. Validation: JECAM experiments12
Ukrainian JECAM site
Cropland mask
Kyiv region

9. Image without filtering; Refined lee filter; C) DCTF; D) MultiLogDCTF.
Classification maps 13
Image without filtering;
Refined lee filter;
C) DCTF;
D) MultiLogDCTF.

10. Filter OA, % / Kappa Results 14 No filter 82.6 / 0.7 Boxcar
86.4 / 0.76
Frost
86.7 / 0.77
Gamma map
86 / 0.75
IDAN
86.2 / 0.76
Lee
86.5 / 0.76
Lee Sigma
87/ 0.77
Median
85.8 / 0.75
Refined Lee
87.4 / 0.78
DCTF
87.7 / 0.78
DCTF HT
87.5 / 0.78
MultiLogDCTF
88.7 / 0.8

11. Conclusions The overall accuracy without any filters is 82.6%;15
The overall accuracy without any filters is 82.6%;
Utilizing of different methods for speckle reducing results in better classification maps - accuracy ranges from 85.8% to 88.7%;
The most accurate and useful filter from ESA SNAP toolbox for crop mapping task is the Refined Lee;
The proposed DCT-based filtering approach has outperformed all available filters in ESA SNAP toolbox;
Importantly, that this filter increases UA and PA for each class, excluding forest and bare land.

12. 16
Thank you!