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

egion number 415 location of the 415 228 ,4 249.4 119.7 313.0 : 173.3 3.5 0.5 100 i 164 3 273 69 4 .1 50.9 .0 49.5 .8 9.0 ire another measure le number of be divided by its :suyama(1980). by deviding the :onvex hull, iphs can be created mcy matrix is :oordinates and the tself. :s of the segmented converted to a ilygons can be consists only of le 2. Tories, where d of the category roperty list of the lation. The results ns are listed in o real correlation ategories. Only by ricultural landuse or rotation of the ese factors are including pure ill only be ined on a higher suyama(1980) i.e. ings, woods, ly there will be reen elongatedness issification first Table 3. Correlation tests on fields with their agricultural class. Explanation: CATEGORY versus Perimeter, Size, Rotation angle as described above, Elongatedness, Fit in MBR, a second rotation measurement, and the means of Channels 1 to 4. First coefficient = Pearson coefficient, Lower coefficient = the significance probability of the correlation. PERIM SIZE ROTI ELONG FIT CAT .1308 .0353 .1179 .0580 .0252 .6861 .0699 .2625 .0142 .8197 CHI CH2 CH3 CH4 ROT 2 CAT .2286 .0002 .3271 .0001 .3164 .0001 .3268 .0001 .0760 .2229 an expert has to be consulted. It is also almost impossible to create a spatial 'logic' rule of neigbourness with these categories in this area. A bridge will be connected to a river, a car will be adjacent to a road, house or a parking lot but what will be the neigbours of a wine field, moorlands or alfalfa? Leaving the spatial 'reasoning' only the multispectral properties of the segments are left. 5 THE CLASSIFICATION Previous attempts by Megier(1984) with the pixel by pixel classification using a maximum likelihood classifier showed that a non weighted average of 51.3 percent of the pixels in the test areas could be classified correctly. Having the 'contours' of each field together with all the image statistical parameters a per-field classification can be applied. Each field is represented by a vector in a multi-dimensional space created by all the attributes from the property list. A clustering algorithm using the "mutual nearest neighbour"(1974) is applied to 1232 regions. Tests with different variables have been carried out clustering down to 40 nodes. The training polygons are used to assign the cluster labels to one of the six classes. Furthermore the ability to separate the training regions itself by the clustering procedure is tested and given in a percentage. Only when the training regions have an 'acceptable' level of identification the results of the test regions have a meaning. In contradiction to the correllation test of form-parameters, having them available, some clusterings have been made to confirm the meaningless of these attributes for agricultural classification in this area. Table 4 gives the results. Being on the slippery path of statistics a few things can be noticed. An ideal situation is simulated, that is starting with only a few training areas and testing the performance on many known fields. Therefore the most important results in table 4 are the columns TR and TE. Column TR shows how good the training fields are chosen for the classification in this scheme. The higher TR the more meaning TE has. The results show that the median of channel 1 or 2 with channel 3, eventually added with FIT (probably accidental), give the best performance and will increase the accuracy compared to the per pixel classification with 15 percent. To this result must be added that the median of channel 2 and 3 can describe 80.1 percent of the test regions. Problems rise when clusters must be assigned to a ground truth class. Almost every cluster will appear in more than 1 class. With a simple Bayesian decision rule a cluster is relabeled with a class. This leads to the result that the biggest class will get the best performance. This is not satisfying. Other (non-parametric and parametric) classifiers must be Table 4. Results of clustering segmented image, where ME = mean of percentages in all classes of correct classification OV = correct classification in perc. of train and test regions TR = perc. of correct classification of training regions TE = perc. of correct classification of test regions LO = num. of classes not possible to assign to a ground truth class CL = num. of clusters not possible to assign to a ground truth class IN = variables used to create vectors - = no sense in calculating because ground truth classes are lost after clustering ME OV TR TE LO CL IN - - 62.8 28.0 1 5 ROTATION - - 55.7 37.1 1 13 ELONGATEDNESS 20.6 45.2 57.7 42.1 0 7 FIT 20.1 40.0 60.4 32.8 0 14 ROT,ELONG,FIT 40.7 66.2 76.2 63.7 0 10 FIT, MED. CH 2,3 32.7 60.0 67.5 57.0 0 16 ROT,ELONG,FIT 43.4 66.0 77.0 62.7 0 10 MEDIANS CHAN. 2,3 42.1 67.4 76.3 64.0 0 9 MEDIANS CHAN. 1,3 40.3 63.4 78.1 58.6 0 9 MEANS CHAN. 2,3 applied to the data. Together with a more precise choice of the ground truth classes and a better segmentation procedure the results will probably improve. 6 SOMETHING ABOUT EXECUTION TIME All programs are developed on a VAX 785 mini-computer in VAX Pascal with some calls to Fortran subroutines (mainly image file I/O). System management allows tasks with a size of 4 Mbt. leaving 4 Mbt. for the system and overhead. The image consists of 232 by 216 pixels. All mentioned times are execution times based on a single use of the system. The edge preserving smoothing uses 40 seconds per iteration. Edge extraction, connecting and cleaning uses 5 minutes. The edge tracking and region storage program works with a 3 by 3 subimage following the inside of a region. This requires a buffer of 3 adjacent lines. Following the edge this buffer has to be updated by changing the index numbering of the buffer or reading in a new line on a not used part of the buffer depending on its direction. The number of I/O's the program has to perform depends on the number of intersections of edges with each scanline. This is very time consuming. Therefore two versions of this program are written. The first program does actually the I/O from disk n-times. The second program reads the edge map into memory and copies the line number into the wanted place of the buffer (memory to memory). The advantage of the first method is that the program can be run virtually on every computer. The second version needs much more memory space but reduces edge tracking time from around 50 minutes to 10 minutes execution time. The number of tracked fields is about 3000. REFERENCES Devijver, P.A. 1974. On a new class of bound on Bayes risk in multihypothesis pattern recognition IEEE Trans, on Computers, vol.70. Freeman, H. 1961. On the encoding of arbitrary geometric configurations, IRE Trans, vol. EC-10, p.266-268, June 1961. Freeman, H. 1974. Computer processing of line drawing images. ACM Comput. Survey, vol.6, p.57-79.

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