Remote sensing e-course

PCA and Classification Technique

Fatwa Ramdani

Geoenvironment, Earth Science, Grad. School of Science

Outline • This course will focus in Principal Component Analysis and

Classification Technique based on remotely-sensed data, SPOT 6 & Landsat 8 OLI. The methods how to analyze and exploit the SPOT 6 Landsat 8 OLI information for Land Use mapping will be illustrated in GRASS open source software.

• In final section will be follow with the exercise and questions to allow student expand their understanding.

Course Goal and Objectives

• Understand the concept of PCA

• Understand formula module function in open source software

• Understand the benefit of PCA in Classification Technique

Intended Audience • University student with basic level of

knowledge in Remote Sensing studies

• Course Requirements:

– Internet access

– GRASS software (http://grass.osgeo.org/grass64/binary/mswindows/native/)

– QuantumGIS software (http://www.qgis.org/en/site/forusers/download.html)

– Download data here ()

1. What is SPOT6?

Launched September 9, 2012, by India's Polar Satellite Launch Vehicle. It is run by Spot Image based in Toulouse, France. It was initiated by the CNES (Centre national d'études spatiales – the French space agency) in the 1970s and was developed in association with the SSTC (Belgian scientific, technical and cultural services) and the Swedish National Space Board (SNSB).

Resources: • http://www.astrium-geo.com/en/143-spot-satellite-imagery

SPOT6 Bands and Products PANCRO band: 1.5 m panchromatic (0.455 µm – 0.745 µm) 6 m multispectral, 4 bands: • blue (0.455 µm – 0.525 µm) • green (0.530 µm – 0.590 µm) • red (0.625 µm – 0.695 µm) • near-infrared (0.760 µm – 0.890 µm) Primary Processing level closest to the image acquired by the sensor: it restores perfect collection conditions. The

sensor is placed in rectilinear geometry, the image is clear of any radiometric distortion. Optimal for clients familiar with satellite imagery processing techniques wishing to apply their own

production methods (orthorectification or 3D modeling for example). Ortho • Georeferenced image in Earth geometry, corrected from off-nadir acquisition and terrain effects. • Optimal for simple and direct use of the image, and for immediate ingestion into a Geographic Information

System. • The standard 3D model used for ground corrections is the worldwide Elevation30 dataset (also known as

Reference3D).

Principal Component Analysis (PCA) is a dimensionality reduction technique used extensively in Remote Sensing studies (e.g. in change detection studies, image enhancement tasks and more). PCA is in fact a linear transformation applied on (usually) highly correlated multidimensional (e.g. multispectral) data.

The input dimensions are transformed in a new coordinate system in which the produced dimensions (called principal components) contain, in decreasing order, the greatest variance related with unchanged landscape features. We can guided purely by the statistical properties of the image itself. The new bands are called components.

PCA has two algebraic solutions:

• Eigenvectors of Covariance (or Correlation) of a given data matrix >> GRASS

• Singular Value Decomposition of a given data matrix

PCA & band ratio’s benefit in classification

Classification based on the band ratio and subsequent supervised classification has the possibility of producing the best result if the spectra of LULC can be fully exploited (the reduction of data without loss of information, prior to classification).

Resources:

• Lillesand, T. M., and R. W. Kiefer. 2000. Remote Sensing and Image Interpretation, 736. New York: John Wiley and Sons.

• Rogerson, P. A. 2002. “Change Detection Thresholds for Remotely Sensed Images.” Journal of Geographical Systems 4: 85–97.

Activities! • Import file using GRASS

• Explore basic statistics

• Displaying the data:

• Produce RGB composite image

• PCA

– Run i.pca module

– Analyze the result

• C

PCA Analysis (for Landsat 8 OLI)

PC has the most positive or negative contribution i.pca input=B1,B2,B3,B4,B5,B7 output_prefix=PCA

• PC1 2553.26 (-0.2998,-0.2313,-0.4177,-0.2342,-0.6702,-0.4221) [91.10%] • PC2 168.11 (-0.4841,-0.2400,-0.3196, 0.6894, 0.3382,-0.1276) [ 6.00%] • PC3 58.00 (-0.3717,-0.2467,-0.3222,-0.5897, 0.3657, 0.4645) [ 2.07%] • PC4 16.27 ( 0.1545, 0.0497,-0.3195, 0.3426,-0.4678, 0.7317) [ 0.58%] • PC5 6.27 (-0.6507, 0.0183, 0.6650, 0.0396,-0.2856, 0.2254) [ 0.22%] • PC6 0.92 ( 0.3005,-0.9084, 0.2743, 0.0572,-0.0494, 0.0596) [ 0.03%] Eigen values, (vectors), and [percent importance]: The band ratio derived from PC1 is B5/B2. Similarly, the results for PC2–PC3 lead to the choice of the ratios B1/B4, and B4/B7, respectively

PCA Analysis (continue for Landsat 8 OLI)

• Thus,

– PC_landsat_1= -0.2998*B1-0.2313*B2-0.4177*B3…

– PC_landsat_2=-0.4841*B1-0.2400*B2-0.3196B3…

– PC_landsat_3= -0.3717*B1-0.2467*B2-0.3222*B3…

How to evaluate percent of importance? Used eigenvalue;

2553.26/2553.26+168.11+58+ 16.27 + 6.27 +0.92=91.09%

PCA Analysis (continue for Landsat 8 OLI)

• PC1 has the highest factor loading of -0.2313 from band 2 (blue), followed by band 4 (red), and then band 1 (coastal aerosol). Therefore, this component is suitable for mapping coastal environment, wetland, and lake environment. Also capable of differentiating soil and rock surfaces from vegetation

• PC2 has the highest factor loading of 0.6894 from band 4 (red), followed by band 5 (NIR), and then band 7 (SWIR). This component can be termed a healthy (dense, vigorous) vegetation component because healthy vegetation reflects highly in near infrared and also some mid infrared energy except in the water absorption zones in this region

• PC3 has the highest factor from band 7 (SWIR), followed by band 5 (NIR), and then band 2 (blue). PC3 can be termed a dark, dry land component due to the high mid infrared factor loading. Separated land and water sharply. Band 7 has strong water absorption region.

Exercise!

• Run i.pca for SPOT6!

i.pca input=spot1,spot2,spot3,spot4 output_prefix=pca.spot

1. Eigen values, (vectors), and [percent importance]:

2. Band Ratio:

?

LULC Classification

#We use the bands ratio result from PCA output • i.group group=spot_group subgroup=spot_sub

input=pc_spot1,pc_spot2,pc_spot3

• i.cluster group=spot_group subgroup=spot_sub sigfile=urban classes=7 report=urban_report.txt

• remember name of file containing signatures: urban

• i.maxlik group=spot_group subgroup=spot_sub sigfile=spot class=spot_class

#Converting raster to vector • r.to.vect -s input=spot_class output=spot_class feature=area

Application

• Mapping coastal environment

– Mangrove forests

– Kelp forest in turbid water

– Submerged vegetation

• Mapping wetland environment

• Mineral exploration in arid environment

Resources

• http://www.crisp.nus.edu.sg/~research/tutorial/opt_int.htm

• http://www.astrium-geo.com/en/4594-spot-6-products

• https://dl.dropboxusercontent.com/u/4437062/CasalPascual_Gema_TD_2012.pdf

• http://www.geocarto.com.hk/cgi-bin/pages1/sep04/p11.pdf

Result; RGB PC123

d.rgb red=pca.spot.1 green=pca.spot.2 blue=pca.spot.3

PCA; Vegetation and Urbanized Area

Classification Result

Quiz?

• Which bands has the most positive or negative contribution from SPOT6?

• The band ratio derived from PC1 of SPOT6 is x. Similarly, the results for PC2–PC3 lead to the choice of the ratios y, and z. What is the band ratio of x, y, and z?

• How to produce map pc_spot1, pc_spot2, and pc_spot3 as band ratio of SPOT6?

Thank you!

Questions?