=Paper=
{{Paper
|id=Vol-2212/paper23
|storemode=property
|title=A research of classification algorithm of spatial information on the basis of methods of persistent homology and random forest 
|pdfUrl=https://ceur-ws.org/Vol-2212/paper23.pdf
|volume=Vol-2212
|authors=Sergey Eremeev,Kirill Kuptsov,Yuri Kovalev
}}
==A research of classification algorithm of spatial information on the basis of methods of persistent homology and random forest ==
<pdf width="1500px">https://ceur-ws.org/Vol-2212/paper23.pdf</pdf>
<pre>
A research of classification algorithm of spatial information
on the basis of methods of persistent homology and random
forest

                    S V Eremeev1, K V Kuptsov1 and Yu A Kovalev 1


                    1
                    Vladimir State University named after Alexander and Nikolay Stoletovs, Gorky street 87,
                    Vladimir, Russia, 600000



                    Abstract. The classification problem of spatial data is one of the most difficult challenges in
                    the field of the analysis and processing of spatial information. A new approach to a solution of
                    the classification problem of spatial data is presented in article. The offered classification
                    technology of objects will be based on algebraic topology, namely on methods of persistent
                    homology. A barcode is a qualifier of a spatial object. It is determined by computation of
                    topological features of a classified object. The distinctive feature of the offered algorithm is its
                    invariance to affine and topological transformations. The research on results of classification
                    algorithm operation on a set of spatial objects of different classes is carried out.



1. Introduction
Automatic digitization of maps is one of global problems in geographic information systems [1, 2, 3].
Questions of identification [4] and classification of cartographical information appear within this
problem. The problem of classification of spatial data on object classes is one of the most difficult in
the field of the analysis and processing of spatial information. Russian and world researchers try to
solve this problem and propose a set of application-oriented solutions. Having studied scientific works
on this subject it is possible to tell that they solve a problem of object classification with various
degree of efficiency. There are different methods of classification of spatial objects.
    The method intended for work with topographic maps of average scale is presented in [5].The main
application is a classification of the area of objects under construction. The method is based on
geometrical structures of data and spatial analytical methods. Advantage is improvement of quality of
automation of cards with areas of objects under construction.
    The problem of classification of spatial data is also relevant for control of information on
exhaustion of reservoirs or, on the contrary, – about their degradation [6]. The technology is applied to
spatial objects which have similar spectral features, but various form. The algorithm is realized for
classification of reservoirs on Alaska and also is used in Bolivia for classification of pastures.
    The analysis of the image is applied in [7] together with network methods of extraction of
information within a problem of creation of digital tourist maps. The algorithm classifies spatial
objects according to the developed rules of simplification and generalization of maps to emphasize
reference points and to reduce a role of less significant objects. The technology is applied to creation
of tourist maps of San Francisco.



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S V Eremeev, K V Kuptsov and Yu A Kovalev




    Processing of satellite images or images with high resolution is made in [8] for classification of the
objects which are contained in them. A classification is made for the main classes of objects which are
presented on topographic maps of large scale.
    An approach using the example of digitizing distribution maps taken from plant-taxonomic atlases
is described in [9]. In result, plant distributions over Europe and Asia have been digitized. The
algorithm is a tool to capture data from maps based on obscure projections.
    The purpose of work is creation of an algorithm for classification of cartographical information
which will make high-quality object classification of various spatial classes and also is invariant [10]
to affine transformations and changes of scale.

2. A classification algorithm of spatial data on the basis of methods of a persistent homology and
random forest
The offered algorithm of classification of objects is based on algebraic topology, namely by methods
of persistent homology. Application of topological characteristics and their analysis is new area of
theoretical researches for tasks of the analysis and processing of spatial information. Information from
aircraft is processed and analyzed. The allocated objects are distributed on spatial classes in
accordance with the classification of spatial information.
    A barcode is taken as the qualifier of a class of a spatial object. It is formed by calculation of
topological features of the classified object. Set of values of color intensity of all object points is
created. Sorting of this set of values according to increase is made. Search of vertices of some
intensity is run step by step. It is noted in the list of vertices when finding such point. If this vertice
appeared in the neighborhood of Moore of already noted point, then they are connected a line. The
triangle is formed at emergence of three such vertices. The number of components (vertices, lines and
triangles) at such approach can change on each step of an algorithm. Emergence of vertice adds a
component. Emergence of the line connecting different components leads to disappearance of
component (two components unite in one). Pass in reversed sequence (on decrease) is the following
stage of an algorithm. At the same time the number of holes and their existence time is counted. The
hole is formed at emergence of a triangle. The filtration list for holes turns out depending on
emergence of new components, their association and other operations. Search of the maximum
number of holes and lines is made by the following step. Barcode of the image of an object is
calculated on the basis of these numbers (fig. 1).




                      (a)                                                   (b)
 Figure 1. Barcodes: (a) – the car and (b) – the P-shaped building. The quantity of holes and intensity
              of points on the color model RGB are displayed on axis X and on axis Y.

   A belonging of object to a spatial class is defined by comparing of barcodes of two objects.
Previously training on images of objects of differrent classes is made (fig. 2, a). Comparing represents
check of inclusion of Bettie numbers (the maximum numbers of holes and lines of the image of an
object) in the range which characterizes objects of a spatial class (fig.3). The algorithm is
complemented with the random forest method [11-13] for optimization of work of an algorithm on


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time. It allows to improve speed of an algorithm. The decision tree is formed on the basis of this
distribution (fig. 2, b).It is result of work of algorithm.




                            (a)                                                     (b)
  Figure 2. (a) Distribution of objects on classes (an algorithm training): A1 –cars (if Y <Y1and
X<X1); A2 –buildings (if Y ≥Y1and X≥ X1); X – maximum quantity of components; Y – maximum
               quantity of holes. (b) Decision tree on the basis of this distribution.




                       (a)                                                       (b)
 Figure 3. Distribution of objects on classes: (a) – current object is a car and (b) – current object is a
building. The maximum quantity of edges and the maximum quantity of holes are displayed on axis X
                                            and on axis Y.

   The random forest model is applied on the basis of values of Bettie numbers which are taken as
features. Random forest is the algorithm consisting of a set of decisive trees. It has been offered by
Leo Breiman and Adele Cutler [14]. The algorithm is implemented according to the following scheme.
   1. Receiving subselection of the training model. Taking of selection of the training row happens at
       this stage. The tree is building on its basis.
   Basic algorithms have to be unique. Each tree is formed on the training selection for this purpose.
       There is an element of randomness at the choice of splittings.
   The more trees, the quality is better. But time of control and work of Random forest increase in
       proportion.
   2. Viewing of set of random features. It is made for realization of splitting of each branch of a tree.
   If the number of features for splitting increases, then time of creation of the forest increases, and
       trees become "more monotonous".
   3. Choice of the best feature and of branch of a tree. Creation of trees is made until disappearance
       of elements from selection.
   Creation of Random forest in direct ratio to selection depth. The selection is deeper, the algorithm
       is longer executed. The quality on training sharply increases at increase in selection depth. But it
       usually increases on control selection.
   It is recommended to use the maximum depth of trees at realization of this algorithm. Change of
       the parameters connected with restriction of number of objects doesn't result in significant effect
       when using superficial trees. Each feature has different degree of importance therefore a part of
       features can not bear advantages.


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3. A research of work of classification algorithm on real spatial objects
The research of the offered algorithm is carried out. Testing of spatial information on the basis of
images from aircraft from some height is executed. Classes of spatial objects such as vehicles and
buildings are considered. The detailing is made on each class. Vehicles such ascars (are presented on
fig.4,a-f), light commercial vehicles and minibuses(fig.4,g-j), buses (fig.4,k-l) are considered.
Rectangular, G-shaped, P-shaped, private types of buildings are presented (fig.5).The quantity of the
training and test selections in the sum is equal 100 images of objects.




          (a)                                       (b)                                  (c)




          (d)                                       (e)                                  (f)




          (g)                                       (h)                                  (i)



         (j)                               (k)                                          (l)
     Figure 4. Spatial class of objects "Vehicles". (a-f) – cars; (g-j) – light commercial vehicles and
                                         minibuses; (k-l) – buses.




                                                            (b)
                                     (a)




         (c)                                                          (d)
Figure 5. Spatial class of objects of "Building". (a) – rectangular; (b) – G-shaped; (c) – P-shaped; (d)
                                            – private houses.

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   Research results are presented in table 1. The algorithm showed the largest accuracy when
determining light commercial vehicles and buses in category of vehicles and for rectangular, G-shaped
and P-shaped buildings (100%).
                         Table 1. Classification of objects by spatial classes.
                                Object class                             Classification accuracy, (%)
                 Vehicles                                                              98.33
                 Cars                                                                     95.00
                 Light commercial vehicles and                                           100.00
                 minibuses
                 Buses                                                                   100.00
                 Building                                                                 97.50
                 Rectangular;                                                            100.00
                 G-shaped                                                                100.00
                 P-shaped                                                                100.00
                 Private                                                                  90.00
    Algorithm is invariant to affine transformations. Therefore tests with the different angles of
shooting are carried out for the offered types of an object (90˚,180˚,270˚, 360˚– fig.6). Research results
of invariancy of an algorithm to affine transformations are presented in table 2.




                         (a)                                                       (b)




                  (c)                                              (d)
Figure 6. A spatial object of the class "Vehicle" in turn on (a) – 90˚; (b) – 180˚; (c) –270˚; (d) – 360˚.

                 Table 2. Classification of objects by spatial classes with turn of the image.
                                  Classification              Classification          Classification      Classification
       Object class            accuracy for turn on         accuracy for turn        accuracy for turn   accuracy for turn
                                    90˚, (%)                  on 180˚, (%)             on 270˚, (%)        on 360˚, (%)
Vehicles                                 100.00                     100.00                   100.00             100.00
Cars                                     100.00                     100.00                   100.00             100.00
Light commercial
                                         100.00                     100.00                   100.00             100.00
vehicles and minibuses
Buses                                    100.00                     100.00                   100.00             100.00
Building                                 100.00                     100.00                   100.00             100.00
Rectangular;                             100.00                     100.00                   100.00             100.00
G-shaped                                 100.00                     100.00                   100.00             100.00
P-shaped                                 100.00                     100.00                   100.00             100.00
Private                                  100.00                     100.00                   100.00             100.00



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    Also an algorithm is invariant to scale. Therefore tests in images of the same area of different
scales are carried out (table 3). The algorithm hasn't made mistake for two subclasses of vehicles
(from three) and for two subclasses of buildings (from four).

      Table 3. Classification of objects by spatial classes with images of an object at different scales.
                                Object class                             Classification accuracy, (%)
                 Vehicles                                                                 96.67
                 Cars                                                                     90.00
                 Light commercial vehicles and                                           100.00
                 minibuses
                 Buses                                                                   100.00
                 Building                                                                 96.25
                 Rectangular;                                                            100.00
                 G-shaped                                                                 95.00
                 P-shaped                                                                100.00
                 Private                                                                  90.00

4. Conclusion
Existing approaches of classification of spatial data are considered in article. New approach of
realization of an classification algorithm on the basis of topological characteristics of the image is
offered. At the heart of an algorithm methods of a persistent homology and the Random Forest method
are put. Results of researches have shown expediency of application of the developed classification
algorithm of spatial information as it invariant to affine transformations and changes of scale.
    A deformation (distortion) of source data is one of problems of map object classification. A
stretching of images of spatial objects is one of types of deformation.The solution of this problem is
important within the solution of a problem of classification of spatial information and automatic
digitization of maps.Algorithm modernization in the sphere of processing of spatial data with
deformations of various types is the following step in improvement of work of the offered algorithm.

5. References
[1] Andrianov D, Eremeev S and Kuptsov K 2017 Identification of spatial objects on digital maps
      CEUR Workshop Proceedings 1940 1-7
[2] Shekhar S and Xiong H 2008 Digitization of Maps Encyclopedia of GIS (Springer, Boston, MA)
[3] Fursov V A, Goshin Ye V and Kotov A P 2016 The hybrid CPU/GPU implementation of the
      computational procedure for digital terrain models generation from satellite images Computer
      Optics 40(5) 721-728 DOI: 10.18287/2412-6179-2016-40-5-721-728
[4] Vizilter Y V, Gorbatsevich V S, Vorotnikov A V and Kostromov N A 2017 Real-time face
      identification VIA CNN and boosted hashing forest Computer Optics 41(2) 254-265
[5] Basaraner M and Selcuk M 2008 A structure recognition technique in contextual generalisation of
      buildings and built-up areas Cartographic Journal 45(4) 274-285
[6] Frohn R C 2006 The use of landscape pattern metrics in remote sensing image classification
      International Journal of Remote Sensing 27(10) 2025-2032
[7] Grabler F, Agrawala M, Sumner R W and Pauly M 2008 Automatic generation of tourist maps
      ACM Transactions on Graphics 27(3) 100
[8] Guienko G and Doytsher Y 2003 Geographic information system data for supporting feature
      extraction from high-resolution aerial and satellite images Journal of Surveying Engineering
      129(4) 158-164
[9] Scholzel C A, Hense A, Hubl P, Kuhl N and Litt T 2002 Digitization and geo-referencing of
      botanical distribution maps Journal of Biogeography 29(7) 851-856



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[10] Fedotov N G, Syemov A A and Moiseev A V 2016 Analysis of conditions that influence the
     properties of the consructed 3D-image features Computer Optics 40(6) 887-894 DOI:
     10.18287/2412-6179-2016-40-6-887-894
[11] Abdulsalam H, Skillicorn D B and Martin P 2011 Classification Using Streaming Random
     Forests IEEE Transactions on Knowledge and Data Engineering 23 22-36
[12] Biau G, Devroye L and Lugosi G 2008 Consistency of random forests and other averaging
     classifiers Journal of Machine Learning Research 9 2015-2033
[13] Hastie T, Tibshirani R and Friedman J 2009 The Elements of Statistical Learning: Data Mining,
     Inference, and Prediction (Springer-Verlag) p 746
[14] Breiman L 2001 Random Forests Machine Learning 45(1) 5-32

Acknowledgment
The reported study was funded by RFBR and Vladimir region according to the research project № 17-
47-330387. The reported study was funded by Vladimir region according to the research project №
326 of 29.09.2017.




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