GIS practical using SPRING

Michael Gould

Lubia Vinhas

Remote Sensing Images

GeoTiffTagged Image File Format is a file format for storing images within a single file. It contains

header tags (size, definition, image-data arrangement, applied image compression) defining

the image's geometry

Remote Sensing Images

GeoTiffTIFF based interchange format for georeferenced

raster imagery. The potential additional information includes projections, coordinate systems, ellipsoids, datums, and everything else necessary to establish

the exact spatial reference for the file.

Remote Sensing Images

GeoTiffTIFF based interchange format for georeferenced

raster imagery. The potential additional information includes projections, coordinate systems, ellipsoids, datums, and everything else necessary to establish

the exact spatial reference for the file.

SPRING – P1

- Create a new SPRING database (remember: it is just a repository!)

- Create a partition where you are going to store your images, and call it CBERS_IMG.

SPRING

- Import the files *Belem_1.tif, Belem_2.tif, Belem_3.tif and Belem_4.tif to your database:- When you import the first file, a new Project will be

created with the bounding box and projection defined in the geotiff

- This images are images from the CCD instrument of the CBERS satellite in 10/08/2008

- http://www.cbers.inpe.br/?hl=en&content=cameras1e2e2b for more information about the instrument

*Belém is the capital of Pará state in the north of Brazil

Remember the reflectance curve

CBERS CCD Bands

Band 1 = 0,45 - 0,52 m (blue)

Band 2 = 0,52 - 0,59 m (green)

Band 3 = 0,63 - 0,69 m (red)

Band 4 = 0,77 - 0,89 m (near infrared)

Band 5 = 0,51 - 0,73 m (pancromatic)

SPRING

- Display each band separately in monocromatic mode

- Find the best (visually) color composition using this bands

Segmentation

Image is partitioned into meaningful regions whose points have nearly the same properties,e.g., grey levels, mean values or textural properties

Segmentation methods

1) Region-based: dividing up the images into a number of homogeneous regions, each having a unique label

2) Edge detection: determining boundaries between homogeneous regions of different properties

3) Combination of region-based and edge detection

The selection of any of these segmentation approaches

greatly depends on the type of data being analyzed and

on the application area

Region Growing

1. Segment the entire image into pattern cells (1 or more pixels).

2. Each pattern cell is compared with its neighboring cells to

determine if they are similar, using a similarity measure. If they

are similar, merge the cells to form a fragment and update the

property used in the comparison.

3. Continue growing the fragment by examing all of its neighbors

until no joinable regions remain. Label the fragment as a

completed region.

4. Move to the next uncompleted cell, and repeat these steps until

all cells are labeled.

Region Growing in SPRING

ParametersSimilarity: Treshold value below which two regions are

considered similar at a given step, considering the similarity as the Euclidean distance between the mean vector of two regions

Area: Minimun number of pixels allowed in an area

OutputNew layer with the labeled regions

Run two segmentation experiments on the images that you have. Compare the results.

Edge Detection

• Edges are boundaries in the image. Are areas with strong intensity contrasts.

Sobel operator

Aproximates the gradient by convolution masks

-1 0 1

-2 0 2

-1 0 1

1 2 1

0 0 0

-1 -2 -1

Gx Gy

G = Gx + Gy

Watershed

The gradient image is considered as a topographic surface

Flood this surface from its minima and, preventing the merging of the waters coming from different sources, two different sets: the catchment basins and the watershed lines

Region Growing in SPRING

ParametersInitial Level: the initial value from where the

flooading starts

Output

New layer with the labeled regions

Run two segmentation experiments on the images that you have. Compare the results.

Classification

The extraction of distincts classes or themes related to land use and land cover categories

Urban

Water

Bare soil

Classifiers

Pixel-By-Pixel: consider each pixel as an object that has to be classified

Region: consider each a homogeneous region (previously delimitaded) as an object to classified

Supervised: samples of known targets are used to describe the spectral signature of the target, and then extrapolated to other areas of unknown targets

Unsupervised: based solely on the image statistics without availability of training samples or a-priori knowledge of the area

Pixel-by-pixel, unsupervised

K-MeansMinimize the variability within each clusterK: number of clusters (themes) that have to be defined.

It is defines a prioryInteractive algorithm:

1. Randomly assign the posicion of the centroid of each cluster

2. Assign each pixel to the nearest cluster3. Recalculate the centroid as the baricenter of the pixels in

the cluster4. Repeat steps 2 to 3 until there is no more modification or

such stop criteria is reached

Region, unsupervised

Each region calculates: mean, variance and area

Regions are ordered decreasing according to its area

A similarity value and a similarity distance is choosen

Assume that the first region defines a theme

For the next region:If it is similar to any of the founded themes assign it to that theme

If not define it as a new theme

Repeat the process until every region has been assigned to a theme

Reclassify again, after recalculating the parameters from the regions generated in the previous steps

Supervised classification

The user indicates which classes should be present in the image and choose samples of each class

Sampling:Samples should be homogeneous and should

represent the entire variability of the classNumber of samples, in general, is >> number of

classesSample analysis should be carried out before the

classification

Supervised classification

Different algorithms, same principle: samples characterize statistically the themeMinimun distance: each theme is characterized by

its mean vector. Pixels are assigned to closest theme according to a Euclidean distance

Maximun Likelihood: assume that themes have a Normal Distribution, that can be descrita by the mean and a covariance matrix calculated from the samples. Each is assigned to the theme that has the maximun probability of contain the pixel

Unsupervised Classification in SPRING

1. If regiona) Execute a segmentation

2. Create a context file3. Define bands to be used in the classification4. If supervised

a) Select samplesb) Analize the samples

5. Run the classification6. Execute post-classification procedures7. Execute mapping to classes

To be continued...