Built-up Extraction from RISAT Data Using Segmentation Approach Chetna Soni*, Manoj Joseph**, A. T. Jeyaseelan** and J R Sharma** *Banasthali University, Niwai **RRSC-W, NRSC/ISRO Jodhpur

6/10/2018 Background Extraction of Built-up area is important in Urban planning, Urban Sprawl mapping and also in analyzing Urban Heat Islands Satellites Remote sensing has the advantage of synoptic view of earth with temporal coverage in assessing the spatial and temporal nature of urbanization In Radar imageries, built up area shows very high back scattering since buildings act as corner reflector which gives multiple bounce Pixel based classification of SAR images give less accuracy due to the presence of noise like speckles but object-based classification deals with objects instead of pixel. Segmentation is an optimization procedure to group the pixels to form objects which minimizes the heterogeneity in neighborhood pixels . Segmentation works on two principles. Bottom up- Grouping of pixels to make larger object. Top down- Cutting down objects to make smaller objects. Objective Extraction of Built-up area from SAR imagery using segmentation approach

Study Area & Data Software Used Data Specification Study Area Jodhpur City and surround area. Data Specification RISAT-1 FRS-1 (Fine resolution mode) DoA : 1st July 2012 Polarisation : Dual pol HH-HV Frequency band : C-band Incident angle : 50.370 Spatial resolution : 8m Software Used ENVI- Sarscape Definiens eCognition/Developer

Built-up in SAR Imagery In SAR imageries built-up is having high backscattering co-efficient than to other features. Backscattering co-efficient values for built-up lies between 7.0 to -7.0 dB Urban Fallow land crop

Methodology Import RISAT Image To improve visualization multi-looking with 3*3m (Range*azimuth) window size has been done. Enhanced frost filtering reduces speckles while preserving texture information of the image. It identifies single image object of pixel size and merges with neighboring objects based on homogeneity criteria (combination of spectral and shape). Scale Shape Compactness Spectral Difference Segmentation refines the previous segmentation by merging objects which are spectrally similar. Maximum Spectral Difference Supervised classification with Nearest Neighborhood algorithm has been done. Merge region algorithm reduces the number of objects by merging the neighboring object of a class. Multi-looking,  Calibration, Layer Stacking, Filtering Multi-resolution Segmentation (Level 1) Spectral Difference Segmentation (Level 2) Training Sites NN Classification Built-up Extraction Merge Region Validation

Scale- Higher value of scale forms larger objects and small value forms smaller one Compactness- Value assign to compactness give relative weighting to smoothness Shape- The shape homogeneity is based on the deviation of a compact (or smooth) shape maximum spectral difference- Define the maximum spectral difference in gray values between image objects Level 1 level 2 Scale 3 compactness 0.2 shape 0.1 maximum spectral difference 0.5 Segmentation level 1 Segmentation level 2

Optimization of segmentation parameters Scale Compactness Shape Scale 10 Compactness 0.8 Shape 0.8 Scale 5 Compactness 0.5 Shape 0.5 Scale 3 Compactness 0.2 Shape 0.2

Classification Results In supervised Classification Training Samples are taken to train the classifier. Class separability is 0.7936 between Built-up and Non built-up Built-up Non built-up Results Extracted Built-up from RISAT Extracted Built-up from LISS-IV Carto Merge

Validation Apartments High court Building 12th July 2012 6/10/2018 Validation 12th July 2012 12th July 2010 Apartments High court Building

Resulted Statistics Land cover Feature RISAT Image (area in sqkm) LISS IV-Carto Merge Image Built-up 61.155 57.651 Back scattering varies due to incidence angle and orientations of buildings.