introduction to image processing for remote sensing ... · università degli studi di roma tor...

Università degli studi di Roma Tor Vergata

Corso di Telerilevamento e Diagnostica Elettromagnetica

Anno accademico 2010/2011

Dr. Matteo Picchiani

[email protected]

07/06/2011

Introduction to image processing for remote sensing:

Practical examples

ESA has developed a series of Software ‘Toolboxes’

Each Toolbox is a collection of OPEN SOURCE software tools to help the

remote sensing community to exploit ESA (and others company) data.

BASIC

ERS&ENVISAT

(A)ATSR AND

MERIS

TOOLBOX

(BEAM)

BASIC

ERS&ENVISAT

ATMOSPHERIC

TOOLBOX

(BEAT)

BASIC

RADAR

ALTIMETER

TOOLBOX

(BEAT)

POLARIMETRIC SAR

DATA PROCESSING

AND EDUCATIONAL

TOOL

(POLSARPRO)

NEXT ESA

SAR TOOLBOX

(NEST)

GOCE USER

TOOLBOX

(GUT)

http://earth.esa.int/resources/softwaretools/

ESA EO User Toolboxes

BEAM is an open-source Java toolbox and development

platform for viewing, analyzing and processing of optical remote

sensing raster data.

BEAM supports different ESA and other EO sensors and

generic data formats.

Supported Instruments Generic EO Data Formats

BASIC ERS&ENVISAT(A)ATSR AND MERIS TOOLBOXis

From http://www.brockmannconsult.de/cms/web/beam/

VISAT - An intuitive desktop application to be used for visualization,

analyzing and processing of remote sensing raster data.

A set of scientific tools (> 11 Data Processors) running either from the

command line or invoked by VISAT, also entirely written in Java.

A rich Java API for the development of new remote sensing

applications and BEAM extension plug-ins.

User support: Tutorials, Plug-Ins, Issue tracker, Community Wiki

(http://www.brockmann-consult.de/cms/web/beam/documentation)

BEAM Components


Image display and navigation even of giga-pixel images

Layer management allows adding and manipulation of overlays such as other

bands, images from WMS servers or ESRI shapefiles

Region-of-interest definitions for statistics and various plots

Band arithmetic using arbitrary mathematical expressions

Reprojection and ortho-rectification to common map projections

Geo-coding and rectification using ground control points

Store and restore the current session including all opened files, views and layers

VISAT


VISAT INTERFACE

Menu Bar

Tool Bar

Image View

Product View

Satellite raster data processing

Enhancing an image or extracting information or

features from an image.

Computerized routines for information extraction

(eg, pattern recognition, classification) from

remotely sensed images to obtain categories of

information about specific features.

Image Processing Includes

Image enhancement and sharpening

Image filtering

Radiometric correction

Geometric correction

Image classification

Pixel based

Object-oriented based

Post-classification and GIS

Change detection

What Is Image Enhancement?

Image enhancement is the process of making images more

useful.

The reasons for doing this include:

Highlighting interesting detail in images.

Change the visualization in order to achieve information.

Making images more visually appealing.

Data Opening:

Data Resizing:

Linear Enhancement

Are enhancement techniques that stretches the

range of image brightness in a non-proportional

manner.

A nonlinear stretch expands one portion of the grey

scale while compressing the other portion.

While spatial information is preserved, quantitative

radiometric information can be lost.

E.g. a logarithmic stretch compresses the higher

brightness values within an image and

disproportionately expands the darker values.

A - Unity Transfer Function

B - Logarithmic Transfer Function

C - Root Transfer Function

D - Exponential Transfer Function

E - Inverse Logarithmic Transfer Function

F - Histogram Equalization Transfer Function

DN - original brightness values

DN' - stretched brightness values

Non-linear Enhancement

Non-linear Enhancement

Spatial Filtering

Low pass 3x3

Spatial Filtering

Low pass 5x5

Spatial Filtering

High pass 3x3

Spatial Filtering

High pass 5x5

Spatial Filtering

Spatial Filtering

Laplace 3x3

Spatial Filtering

High pass 3x3

Laplace 3x3

Spatial Filtering

False Colour Composites

Electromagnetic Spectrum

Remote sensing images

are taken within specific

spectral regions.

The spectral bands of the LANDSAT Thematic Mapper

LandSat TM produces 7 digital images of a scene representing the 7 bands of

electromagnetic energy captured

False Colour Composite Steps:

* DATA FILE NAMES

The file naming convention for Landsat 7 GeoTIFF is as follows:

L7fppprrr_rrrYYYYMMDD_AAA.TIF where:

L7 = Landsat-7 mission

f = ETM+ data format (1 or 2)

ppp = starting path of the product

rrr_rrr = starting and ending rows of the product

YYYYMMDD = acquisition date of the image

AAA = file type:

B10 = band 1

B20 = band 2

B30 = band 3

B40 = band 4

B50 = band 5

B61 = band 6L (low gain)

B62 = band 6H (high gain)

B70 = band 7

B80 = band 8

MTL = Level-1 metadata

TIF = GeoTIFF file extension

False Colour Composite Steps:

Band ratioing

Ratioing is an enhancement process in which the DN value of one band is divided

by that of any other band in the sensor array. If both values are similar, the

resulting quotient is a number close to 1. If the numerator number is low and

denominator high, the quotient approaches zero. If this is reversed (high

numerator; low denominator) the number is well above 1. These new numbers can

be stretched or expanded to produce images with considerable contrast variation

in a black and white rendition. Certain features or materials can produce distinctive

gray tones in certain ratios.

This ratio distinguished vegetation, water and croplands. It has

enhanced forests, barren lands. Because forests or vegetation exhibits

higher reflectance in near IR region (0.76 -0.90 um) and strong

absorption in red region (0.63-0.69u m) region. This ratio uniquely

defines the distribution of vegetation. The lighter the tone, the greater

the amount of vegetation present.

TM4/TM3:

TM4/TM3:

Normalized Difference Vegetation Index (NDVI):

This is a commonly use vegetation index which uses the red and

infrared bands of the EM spectrum.

NDVI = (NIR-Red)/(NIR+Red)

Normalized Difference Vegetation Index (NDVI):

Display a histogram of a vegetated area

ROI Selection

Display a histogram of a vegetated area

MERIS is a programmable, medium-spectral

resolution, imaging spectrometer operating in the

solar reflective spectral range. Fifteen spectral

bands can be selected by ground command.

The instrument scans the Earth's surface by the so

called "push-broom" method. Linear CCD arrays

provide spatial sampling in the across-track

direction, while the satellite's motion provides

scanning in the along-track direction.

MERIS is designed so that it can acquire data over

the Earth whenever illumination conditions are

suitable. The instrument's 68.5° field of view around

nadir covers a swath width of 1150 km. This wide

field of view is shared between five identical optical

modules arranged in a fan shape configuration.

From : http://envisat.esa.int/instruments/meris/

Meris

MDS

Nr.

Band

centre

(nm)

Bandwidth

(nm)Potential Applications

1 412.5 10 Yellow substance, turbidity

2 442.5 10 Chlorophyll absorption maximum

3 490 10 Chlorophyll, other pigments

4 510 10 Turbidity, suspended sediment, red tides

5 560 10 Chlorophyll reference, suspended sediment

6 620 10 Suspended sediment

7 665 10 Chlorophyll absorption

8 681.25 7.5 Chlorophyll fluorescence

9 705 10 Atmospheric correction, red edge

10 753.75 7.5 Oxygen absorption reference

11 760 2.5 Oxygen absorption R-branch

12 775 15 Aerosols, vegetation

13 865 20 Aerosols corrections over ocean

14 890 10 Water vapour absorption reference

15 900 10 Water vapour absorption, vegetation

Meris Spectral Bands

http://www.brockmann-consult.de/cms/web/beam/meris-products

Meris False Colour Composite:

False Colour Composite BEAM defaults:

BEAM default MERIS NDVI function:

Use of Masks

Meris cloud probability processor

Image reprojection

Map projection: this step allows to go from geographic coordinates to some

specific cartographic projection as Lambert, Mercator or UTM.

For maps of the Earth, a projection consists of a graticule of lines representing

parallels of latitude and meridians of longitude or a grid.

Image reprojection

Orthorectification is the process by which the geometric distortions of

the image are modeled and accounted for, resulting in a planimetricly

correct image. To put it another way, our 3D world is imaged by most

sensors in 2D and orthorectification corrects for many of the anomalies

resultant from this conversion. Orthorectified imagery is particularly

useful in areas of the world with exacerbated terrain features such as

mountains, plateaus, etc.

The orthorectification process yields map-accurate images which can

be highly useful as base maps and may be easily incorporated into a

GIS. The success of the orthorectification process depends on the

accuracy of the DEM and the correction formulae. In the case of the

data provided by GLCF, the most accurate publicly available DEM was

used and an RMS error of 50 meters or better can be expected.

Image Orthorectification

http://www.landcover.org/library/pdf/PERSMarch_04_313-322.pdf



Image Orthorectification

introduction to image processing for remote sensing ... · università degli studi di roma tor...

Documents