urban monitoring at a regional scale based on meris and ...€¦ · urban monitoring at a regional...

Urban Monitoring at a Regional Scale Based on MERIS and ASAR data

Luis Gómez-Chova(1) , D. Fernández-Prieto (2) , J. Calpe (1) , and G. Camps-Valls (1)

(1) University of Valencia, GPDS, Dr. Moliner 50, Ing. Electronica, E-46100, Burjassot (Valencia), Spain

(2) European Space Agency, ESA-ESRIN, Via Galileo Galilei, EO Science & Applications, 00044, Frascati (Rome), Italy

IntroductionIntroduction

Supervised Methods Unsupervised Methods

Training Set Whole Image

Available?Exhaustive?

Representative?Meaning of clusters?

IntroductionOne of the main applications of remote sensing images is classification of land covers.

IntroductionIntroductionSometimes users are not specially interested in obtaining an exhaustive map with all the thematic classes present in an area.

In some key operational domains –e.g. forestry or urban areas mapping– they want to identify accurately only one class –or few classes–.

Partially supervised methods analyze this practical point of view using only information of the Class of Interest (CoI)

Introduction

IntroductionIntroductionEarth Observation platforms allow the monitoring of urban expansion with the required spatial and temporal resolution.

ESA ENVISAT platform offers simultaneous acquisition of MERIS (MEdium Resolution Imaging Spectrometer) and ASAR (Advanced Synthetic Aperture Radar) sensors:

• The information retrieved by optical and SAR sensors differs to a great extent one from another (spectral, structural and temporalfeatures)

Introduction

ObjectivesObjectivesThe aim of this work is to demonstrate the capabilities of MERIS and ASAR data to map urban areas at a regional scale.

• To obtain high accuracy at a regional scale(MERIS FR works with a 300m pixel size)

• To provide the basis for a low-cost highly automated pan-European service dedicated to identifying and monitoring urban area.

It is not interesting to obtain an exhaustive map with all the thematic classes present in an area, but to produce an automatic classification of ‘Urban/Non-Urban’.

Introduction

Objectives

ObjectivesObjectivesFor this purpose, a two-stage classification scheme based on an one-class partially supervised approach is proposed:

• It allows us to introduce supervised information (at least in one reliable feature) about the class of interest without an additional sample labeling.

Constrains: • It must not need the probability density function of the

class of interest (urban class change from place to place).• It must not assume any distribution to the classes

(heterogeneous class & multi-sensor data).

Assumption:• The user knows the range (threshold values) of at least one

feature for the class of interest.

Introduction

Objectives

ObjectivesObjectivesOne-class partially supervised approach which allows:

• introduce supervised information • without an additional sample labeling

Introduction

Objectives

Featuresubset

Training/ValidationInputSpacePreprocessing

& Data Fusion

SARData

OpticalData

Feature Selection/Extraction

‘Urban/Non-Urban’Classification

AccuracyTest

Whole Images

Knowledge-based Feature Extraction

Using features not related to urban information, unsupervised methods build clusters that could be not related to the‘Urban/Non-Urban’ areas.Therefore, analysis of the multispectral and SAR data characteristics over urban areas is mandatory to define good features that optimize the data separability in the input space.

One-class Partially Supervised method

MaterialMaterialData used in this work were obtained under framework of the ESA Category-1 project (C1P-ID2489)

The test site is Naples at 2003:• Three multitemporal ASAR SCL images (IS2)

the months of June, July, and August.• Two MERIS FR L1 images of July and August• The Ground Truth of urban areas.

Introduction

Objectives

Material

Material: Ground TruthMaterial: Ground TruthCORINE Land Cover 2000 has been used as a Ground Truth: What is an urban area?

Introduction

Objectives

Material

Material: Ground TruthMaterial: Ground TruthIntroduction

Objectives

Material

Material: MERIS dataMaterial: MERIS dataMERIS FR Level 1 images contain TOA radiance. These images were atmospherically corrected using the method presented by Luis Guanter et. al. during this workshop.Corrected images (Surface reflectance) were projected in the same projection than the ASAR images using the BEAM software (upscaling from 300m to 12.5m using a nearest neighbor interpolation).A refinement of the coregistration of both sensors was performed using ground control points (GCP)

Introduction

Objectives

Material

Material: MERIS dataMaterial: MERIS dataIntroduction

Objectives

Material

RGB composite of MERIS surface reflectance


Objectives

Material

TOA Radiance Surface Reflectance

TOA radiance or surface reflectance?


Objectives

Material

TOA Radiance (b1-b8)/(b1+b8) Surface Reflectance

High concentration of aerosols and pollutants over

urban areas

Material: ASAR dataMaterial: ASAR dataESA ASAR SCL products were geocoded with the Envisat ASAR Geocoding System (EGEO) of DLR:

• Slant range (time) to Ground range (spatial) projection by a multilooking x 5.

• Images are Enhanced Ellipsoid Corrected (EEC) using the "GLOBE" DEM (1km x 1km)

• Amplitude images and a Layover&Shadow mask were provided with a resolution of 12.5 m.

Interferometric Coherence was computed using the ESA BEST software and coregistered and geocoded with the same parameters than the amplitude images.Processed images were compared with a SRTM-X SAR amplitude grid: horizontal accuracy < 20m.

Introduction

Objectives

Material

Material: ASAR dataMaterial: ASAR dataIntroduction

Objectives

Material

Images processed with the collaboration of Martin Huber of the DLR!

Feature ExtractionFeature ExtractionIntroduction

Objectives

Material

Feature Extraction

Extraction of good features in order to optimize the ‘urban/non-urban’ separability in the input space.

Featuresubset


& Data Fusion

SARData

OpticalData



AccuracyTest

Whole Images

Knowledge-based Feature Extraction

MERIS FR (300 m): ‘urban class’ presents a very heterogeneous spectral distributionASAR (12.5 m):SAR images are complex to process and present speckle (multiplicative noise)

Feature Extraction: MERISFeature Extraction: MERISIntroduction

Objectives

Material

Feature Extraction

For multispectral data, difference measures are preferred (continuum removal, band ratios, absolute indexes), since they are resistant to variations in illumination and atmospheric conditions:

• Normalized Vegetation Index (NDVI): (b13-b8)/(b13+b8).

Feature Extraction: MERISFeature Extraction: MERISIntroduction

Objectives

Material

Feature Extraction

The spectral information can be useful to simplify the analysis by masking covers without any relation to our study (water bodies); and to identify areas where the optical information is not reliable and only SAR data can be used (cloud covers).

• Water mask: ratios between blue and NIR bands (strong water absorption over 800 nm).

• Cloud mask: based on the brightness and whiteness of the spectrum and the atmospheric absorption of (O2 and H2O) corrected by the DEM (work presented at poster session).

Feature Extraction: ASARFeature Extraction: ASARIntroduction

Objectives

Material

Feature Extraction

Urban areas: high coherence (stability) and bright reflections(planes at 90º), maintained in time and under varying angles.

Images were filtered to reduce the speckle and maximize the value of local areas with high values (intensity/coherence).

• It improves classification results but implies a reduction of the effective resolution (filter window size).

Feature Extraction: ASARFeature Extraction: ASARIntroduction

Objectives

Material

Feature Extraction

In order to have similar class p.d.f. for both data types(multispectral and SAR) in the feature space, we apply logarithms to the SAR intensities obtaining a Gaussian distribution (approx. SAR backscattering to a log-normal pdf).

Feature Extraction: MERIS&ASARFeature Extraction: MERIS&ASARIntroduction

Objectives

Material

Feature Extraction

Before the classification, we use both sensor information by:• Masking covers without any relation with our study (water

bodies). • Marking the areas where the optical information is not

reliable and only SAR data will be used (cloud covers). • Marking the areas where the SAR information is not

reliable and only multispectral data will be used (shadowed slopes & layover).

MethodologyMethodologyIntroduction

Objectives

Material

Feature Extraction

MethodologyFeaturesubset


& Data Fusion

SARData

OpticalData



AccuracyTest

Whole Images

How to include information about the urban class only in one or two reliable features? How to use all the other available features without decrease the urban classification accuracy?

One-class Partially Supervised method

One-class partially supervised approach which allows:• introduce supervised information • without an additional sample labeling

MethodologyMethodologyBased on hierarchical clustering:

• Idea: it joins the most similar samples until only two clusters remain.

• Drawbacks: higher computational cost, overlapped classes, and it can not detect by itself the class of interest.

In order to solve these limitations we have adopted a two-stage classification scheme:

• Clustering of the image: to find a reduced number of prototype vectors that represent the data set.

• Partially supervised hierarchical joint of clusters: the information about the CoI

Introduction

Objectives

Material

Feature Extraction

Methodology

MethodologyMethodologyTwo-stage Partially Supervised Method

Unsupervised Clustering

InputData

Partially Supervised Hierarchical Clustering

Classification of the whole data set

Previous Knowledge:Range of one feature

only for the CoI

Introduction

Objectives

Material

Feature Extraction

Methodology

MethodologyMethodologyUnsupervised Clustering: SOMSelf-Organizing Maps (SOM) algorithm is used, but other vector quantization algorithms could be applied instead of SOM: K-means, LVQ …SOM iteratively adapts its units, mk, adjusting the nearest unit mc and its neighbors closer to xi

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

MethodologyMethodologyUnsupervised Clustering: SOMSOM produces a set of prototypes that reflects the data properties (distribution in the input space).The number of prototypes, M, can be limited by the user and “independent” of the size of the image, N.

N = 4000 distributions M = 200

Constraint: M should be overestimated (bigger than the number of subclasses present in the scene)

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

MethodologyMethodologyPartially Supervised Hierarchical Clustering: PSHCHierarchical clustering: to group together the found clusters (SOM units) using a Similarity Criterion.

Feature with info of CoI

CoIFMF

CoNI

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Similar clusters

MethodologyMethodologyPartially Supervised Hierarchical Clustering: PSHCHierarchical clustering: to group together the found clusters (SOM units) using a Similarity Criterion.

Feature with info of CoI

CoIFMF

CoI

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Similar clusters

Experimental setupExperimental setup

InputSpacePreprocessing

& Data Fusion

SARData

OpticalData Water Mask

Cloud MaskSAR Shadows and Layover

Feature Extraction:CoherenceNDVIMultitemporal SAR amplitudeMERIS bands

Unsupervised Clustering:

SOM

Partially Supervised Hierarchical Clustering

Classification of the whole images

Previous Knowledge:Coherence range for

urban areas

Maximum of 100 SOM units

Uniform grid spanned by the 2 eigenvectors with greatest eigenvalues (PCA)

Supervised information only for the Coherence map and the NDVI

Average Classification Accuracy for all the scenes

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

ResultsResultsIntroduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

NDVI Cohe NDVI & Cohe.

All Features

NDVI < 0.3 74.42 - 77.34 79.26

Cohe.> 0.3 - 72.93 81.35 81.55

NDVI & Cohe. - - 80.63 82.27

Used features

User defined

threshold

Average Classification Accuracy (at 37.5 m pixels) using different number of features

ResultsResults


All Features

NDVI 74.42 - 77.34 79.26

Cohe. - 72.93 81.35 81.55

NDVI & Cohe. - - 80.63 82.27

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

ResultsResults


All Features

NDVI 74.42 - 77.34 79.26

Cohe. - 72.93 81.35 81.55

NDVI & Cohe. - - 80.63 82.27

Trade off between the quality of the features and the scale of the sources

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

ResultsResultsUrban monitoring with ENVISATCapability of MERIS FR (300m) and ASAR (12.5m)to classify urban areas.

• The objective are urban agglomerations, not a detailed classification of different urban classes.

In this context, a main objective is to investigate the relationship between accuracy and scale.Two approaches are followed:

• To simulate data acquired by a sensor withsmaller resolution (MERIS RR)

• To analyze the decrease of classification accuracy when considering the ground truth at the original full resolution.

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

ResultsResultsOne scheme simulates data acquired by a sensor with smaller resolution taking into account the information loss.

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results


Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

37.5m

150m

300m

1000m

Downscaled GT Original GTClassification


Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

Downscaled GT: Classification given a thresholds in NDVI&Cohe

37.5 150 300 1000

90%

80%

70%


Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

Original GT: Classification given a thresholds in NDVI&Cohe

37.5 150 300 1000

90%

80%

70%

ConclusionsConclusions

Capabilities of ENVISAT MERIS and ASAR data to map urban areas have been analyzed.

Extraction of good features in order to optimize the ‘urban/non-urban’ separability in the input space was performed.

We present a partially supervised classification method that faces the problem of identifying accurately only a CoI.

The operator introduces the information that defines the class of interest without additional sample labeling.

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

Conclusions

ConclusionsConclusionsGood performance in the classification of urban areas usingMERIS and ASAR images:

• 84% using only a threshold in the coherence map • ~0.30 threshold value for NDVI and Coherence

Resolution of MERIS is a critical issue depending on the application requirements:

• MERIS FR can be used with ASAR data in order improve results without reducing drastically the effective classification resolution.

• MERIS RR should be not capable of give products at local or regional scale (< 1 km)

Introduction

Objectives

Material

Feature Extraction

Methodology

SOM

PSHC

Experimental Setup

Results

Conclusions

Thanks for your attention.

MERIS

ASAR

urban monitoring at a regional scale based on meris and ...€¦ · urban monitoring at a regional...

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