Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Damen, M. C. J.

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Per-field classification of a segmented SPOT simulated image. J. H. T. Stakenborg

Document type:: Multivolume work

Structure type:: Chapter

76 well but most of them disturb fine linear features in the image as roads and small creeks. The image will show almost no change after 4 iterations. 2.2 Edge extraction After smoothing edges are extracted from the scene by using the same set of patterns. If the pattern is defined the pixel is set to an edge (background). Result is an edge map with a lot of loose ends showing the weakness of the method. Therefore edge extraction through contouring is under development. Removing these unclosed boundaries is necessary to speed up edge tracking and labeling. Before cleaning a last attempt to save borders is performed by a program connecting ends not further away than 1 pixel. The rest is lost. However, in the case of the SPOT data this connecting and cleaning leads only in a very few cases to elimination of 'significant' edges. This is checked with a visual interpretation of the image. An example of an edge extraction is projected over the original image in figure lb. 2.3 Edge tracking and labeling The developed edge tracking program works as follows. Starting with a cleaned edge map processing begins in the upper left part of the image. Tracking starts at the upper left part of a segment following its inner border and storing the border itself. Back on its starting point it closes the polygon and submits it into a procedure that adaptively masks the region with a certain value. If this region is already masked before, in case of an inclusion, the masking value is set one value lower. If the region has inclusions masking value will be set to a higher value, depending on its own depth of inclusion. In this way pure statistics can be calculated over the image polygons starting on the deepest levels and masking the region when ready. Optionally the boundary will not be included in the statistics when set to the normal or starting masking value. In masking the image the size of the polygon is calculated. The user can define a minimum size for definitive storage. Figure If shows in which order the segments are tracked. The segments are filled with their sequential processing number (labels) modulo 256. Such a picture is very useful to find back the segment number after edge tracking. 3 THE PROPERTY TABLE Polygons are stored in a property table with a signalised chaincode similar to Freeman(1961). Attributes in this list are: Region number, location of starting point in image, perimeter, size, rectangle coordinates, center of mass, rotation of the longest side of the Minimum Bounding Rectangle, rotation of the axis connecting the outmost points of a region (R0T2) , elongatedness (longest side divided by shortest side of MBR), fit(area of polygon divided by area of MBR), number of channels filled with statistics, inclusion index, ten means, medians and standard deviations and the number of chaincodes used. The values of the attributes of one region are given in table 1. The attributes have a fixed record length and are glued with the polygon string to a record with variable length. An accessory index file contains region number, byte offset to record and recordlength to provide direct access through some assembler routines. In this way minimum storage is required. Region image statistics can be applied with any image underlying the regions, even digitized documents. Region polygon form analyses make use of relatively simple algorithms bases on chaincode Table 1. Example of attributes of region number 415 stored in a property table. For the location of the region see fig. lb. Region number: 415 Start points line, elem: 160 228 Center (line/element axis): 156.4 249.4 Perimeter (x30m.): 119.7 Size (30x30 sqm.): 313.0 Rotation angle (E-N-W;0-180 degr.): 173.3 Elongatedness : 3.5 Fit: 0.5 Inclusion index: 100 Normal rectangle; minli, maxli 148 164 minei, maxel 228 273 Number of chaincodes used : 69 Image statistics Chan, nr.: 1 2 3 4 Means : 42.2 45.5 161.1 50.9 Medians : 36.4 38.0 161.0 49.5 Stand. dev .: 20.5 20.4 8.8 9.0 handling(Freeman 1974). In the future another measure will be taken for elongatedness. The number of iterations to shrink a polygon will be divided by its size, also applied by Nagao and Matsuyama(1980). Irregularity can also be described by deviding the size of the region by that of its convex hull. From this table new tables or graphs can be created for other purposes. A region adjacency matrix is simply derived from the rectangle coordinates and the common boundaries of the polygons itself. Figures lc,d,e are showing subsets of the segmented image filled with some attributes converted to a greylevel. 4 LEARNING FROM THE GROUND 'TRUTH' In the same way the ground truth polygons can be stored in a list. The ground truth consists only of agricultural classes listed in table 2. Table 2. Agricultural landuse categories, where NTR = number of training regions, NTE = number of test regions, NHA = number of hectares. category NTR NTE NHA 1. Wine 11 99 99.6 2. Wheat 11 59 55.4 3. Bare soil 6 33 23.6 4. Moorlands 8 4 18.2 5. Alfalfa 7 10 10.7 6. Fallow. 8 19 13.6 Total 51 224 221.1 First the correlation is calculated of the category with all other attributes in the property list of the ground truth using a Pearson correlation. The results of all the 259 ground truth polygons are listed in table 3. Obviously there seems to exist no real correlation between the form factors and the categories. Only by chance there will appear inside agricultural landuse a relation to size, elongatedness or rotation of the main field axis because most of these factors are historically determined. Therefore including pure form factors in a classification will only be fruitfull when the classed are defined on a higher level as is shown by Nagao and Matsuyama(1980) i.e. separation of roads, rivers, buildings, woods, agricultural areas, etc. Undoubtedly there will be areas with a real correlation between elongatedness and class but to use them in a classification first Table 3. C. agriculture Perimeter, Elongatedm measuremen First coef: Lower coef: the correli PERI CAT .130 .035 CH CAT .228 .000 an expert 1 It is ali 'logic' ru] this area, car will b« lot but whi moorlands ( Leaving 1 multispecti 5 THE CLASS Previous at pixel class classifier percent of classified field toget parameters Each field multi-dimer from the pi A clustei neighbour"( with differ clustering The trair cluster lat the abilitj by the clus percentage. 'acceptable the test re the correli available, the meaning classificat results. Being on things can simulated, areas and t fields. The table 4 are how good t! classificat meaning TE channel 1 c FIT (probat and will ir pixel class must be add describe 80 rise when c class. Almo 1 class. Wi cluster is result that performance (non-parame

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