Contextual descriptors ad neural networks for scene analysis in VHR SAR images

Transcription

1 Workshop Nazionale La Missione COSMO-SkyMed: Stato dell Arte, Applicazioni e Prospettive Future Roma, Novembre 2017 Contextual descriptors ad neural networks for scene analysis in VHR SAR images Matteo Picchiani 1, Fabio Del Frate 2, Alessia Falasco 2, Giovanni Schiavon 2 1 GEO-K srl, Via del Politecnico 1, Rome, Italy 2 Univ. degli Studi di Roma Tor Vergata, Via del Politecnico 1, Rome, Italy

2 INTRODUCTION The development of SAR technology during the last decade have made it possible to collect a huge amount of data over many regions of the world. In particular, the availability of SAR images from different sensors, with metric or sub-metric spatial resolution, offers novel opportunities in different fields as land cover, urban monitoring, soil consumption etc. To deal with this information in an efficient and fast way automatic approaches become crucial.

3 FRAMEWORK Increasing number of VHR SAR sensor (ASI Cosmo SkyMed, DLR TERRASAR-X etc.) Production of data catalogs with increasing dimension that in many cases could be difficult to be fully exploited Increasing the effort in the development of novel classification algorithm I. Basic information to query a scene II. Basic statistics over World regions III. Soil changes at medium to large scale IV.. Pixel based Requires high accuracy Time consuming Spotlight Cosmo-SkyMed image ASI (4230x2500 pixels).

4 PATCH BASED APPROACH In the VHR scale, small parts of very different objects have the same scattering mechanism and lookpatches very similar represent hencesmall generating ensembles possible of pixels confusion and incan image be classification. characterized by specific features. On the other hand, understanding if a certain area belongs to a specific land cover class might be less complicated by considering a surface of tens or hundreds of meters. This resembles what is performed by Bag of Words-based techniques for object definition. The patches play the The same use ofrole an image-patch-based of the words in the approach vocabularyasover onewhich of the the most suitable analysis for fast of the and document automatic is performed. analysis of the SAR observed scene may be recommended. COSMO-SkyMed ASI

5 METHODOLOGY COSMO-SkyMed ASI VHR SAR Data Catalog 2 COSMO-SkyMed L1D (GTC) Spotlight prod. VV pol. Naples (Italy) Mari Menuco Lake (Argentina) 3 COSMO-SkyMed L1D (GTC) Spotlight prod. HH pol. Budapest (Ungheria) Mexico City (Mexico) Singapore (Singapore State) Feature extraction step: I. GLCM (benchmark) II. FFT based approach III. Auto-encoding Classification step: I. NN classificator II. 5 inputs III. 4 Classes: Buldings Coast Water Vegetation (Natural surfaces)

6 1. Image extraction from catalog COSMO-SkyMed ASI x200m 2 patch extraction 69 patch for each class 3. Feature extraction PROCEDURE II. III. Fourier Auto-encoder Approach considers the Auto-Associative spectral I. Gray information Level NN Co-Occurrence extracted in order from to reduce the Matrix pixelnonlinear inside a redundancies patch. 5 parameter over aselected: patch. considers the textural information extracted from the backscattering values of the pixel inside a patch. The information related to the texture of the image can be represented by the [2] matrix of relative frequencies with which two neighboring resolution cells, with different gray tones. Mean and variance First and second order statistcs of the spectra 5 measure selected: mm xx, yy = 1 MM NN SS xx,yy (rr, aa) rr=1 aa=1 MM σσ 2 xx, yy = 1 MM NN rr=1 NN aa=1 MM Mean Contrast Each patch extractedcc rr(xx,yy) from = MM rr=1 the NN aa=1 SAR SS xx,yy (rr, image aa) rr has been treated with a Variance MM [4] rr=1 NN aa=1 SSDissimilarity xx,yy (rr, aa) mean filter in order to reduce the effective dimensionality in input to Homogeneity CC aa(xx,yy) = MM rr=1 NN the net of a factor 10 aa=1 SS xx,yy (rr, aa) aa [5] NN SS xx,yy rr, aa mm(xx, yy) 2 Spectral Flux Characterizes the smoothness ofthe spectra MM NN FF xx,yy (rr, aa) = NN xx,yy rr, aa NN xx 1,yy (rr, aa) 2 rr=1 aa=1 Encode of the Spectral centroid inrange inputs andazimuth represents a measure of spectral brightness MM rr=1 NN aa=1 SS xx,yy (rr, aa) [1] [3]

7 PROCEDURE NN Training Overall Accuracy: 76% GLCM Dataset NN Training Overall Accuracy: 80% FFT Dataset NN Training Overall Accuracy: 69% AANN Dataset

8 RESULTS SAR subset of Singapore acquisition (HH pol.) Optical data of the same area imaged by SAR Overall Accuracy: GLCM NN = 76% FFT NN = 80% AANN NN = 68% COSMO-SkyMed ASI GLCM NN Classification FFT NN Classification AANN NN Classification

9 RESULTS SAR subset of Naples acquisition (HH pol.) Optical data of the same area imaged by SAR Overall Accuracy: GLCM NN = 88% FFT NN = 91% AANN NN = 71% COSMO-SkyMed ASI GLCM NN Classification FFT NN Classification AANN NN Classification

10 APPLICATION: DATA MINING Data Mining The process of collecting, searching through, and analyzing a large amount of data in a database, as to discover patterns or relationships. Big Data An accumulation of data that is too large and complex for processing by traditional database management tools Big Data procedures are not still well defined for application on remote sensing database The presented approach can be considered to extract information from large catalog of SAR images, in a simple and accurate manner

11 APPLICATION: DATA MINING Images Classes Classification (%) Ground truth (%) Naples subset 1 Building 40,35 46,78 Vegetation 40,64 32,46 Water 15,20 15,79 Coast 3,80 4,97 Naples subset 2 Building 50,58 57,31 Vegetation 48,25 42,69 Water 0,00 0,00 Coast 1,16 0,00 Assessment of the impact of urbanization on the environment Search for images with percentage of urbanized is higher to a certain threshold Building(%) > 50% Naples 2 Mari Menuco Lake Building 7,60 7,60 Vegetation 44,15 30,59 Water 47,66 47,66 Coast 0,58 6,14 Budapest Mexico city Budapest Building 86,26 84,50 Vegetation 9,06 5,85 Water 4,68 9,65 Mexico city Building 99,12 98,24 Vegetation 0,58 1,75 Water 0,29 0,00 Singapore Building 38,01 38,89 Vegetation 26,22 23,32 Water 25,73 27,78 Coast ,01 Building(%) > 90% Mexico city

12 APPLICATION: DATA MINING Images Classes Classification (%) Ground truth (%) Naples subset 1 Building 40,35 46,78 Vegetation 40,64 32,46 Water 15,20 15,79 Coast 3,80 4,97 Naples subset 2 Building 50,58 57,31 Vegetation 48,25 42,69 Water 0,00 0,00 Coast 1,16 0,00 Monitoring of natural surfaces Find data with lower vegetation fraction Vegetation (%) < 40% Mari Menuco Lake Building 7,60 7,60 Vegetation 44,15 30,59 Water 47,66 47,66 Coast 0,58 6,14 Budapest Building 86,26 84,50 Vegetation 9,06 5,85 Water 4,68 9,65 Mexico city Building 99,12 98,24 Vegetation 0,58 1,75 Water 0,29 0,00 Singapore Building 38,01 38,89 Vegetation 26,22 23,32 Water 25,73 27,78 Coast ,01 Mexico City Budapest Singapore

13 CONCLUSIONS The approach used has allowed to obtain high levels of accuracy in the classification of the images (> 90%), highlighting the validity of the method for the SAR data exploitation. The study carried out is well suited for applications in the field of Data Mining and Big Data. Three different feature extraction techniques (GLCM, FFT and AANN) Best performance achieved by the FFT and it has been used to test a generic data mining services The worst performance of the AANN approach may be due to the not optimal decimation pre-processing of the patches, this point needs to be further investigated Further steps Addition of new parameters, such as the entropy, able to characterize in more specific patches of the image Concurrent use of different features extraction techniques Definition of new classes like airports, bridges, railway stations, etc.

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