Available online at www.sciencedirect.com
ScienceDirect Procedia Computer Science 103 (2017) 388 – 395
XIIth International Symposium «Intelligent Systems», INTELS’16, 5-7 October 2016, Moscow, Russia
Use algorithm based at Hamming neural network method for natural objects classification O.I. Khristoduloa, A.A. Makhmutova,*, T.V. Sazonovab b
a Ufa State Aviation Technical University, Ufa, Russia Orenburg State University Kumertau branch, Kimrtau, Russia
Abstract
This article discusses the practical use of the method of Hamming neural network for classification of the natural objects. The use of this method grounded as accurate as possible the results of the identification of a large numbers of complex composition of vegetation classes, soil covers, water bodies, areas, devoid of vegetation, as well as identifying areas burned by fire, and areas in which there are felling of coniferous and deciduous wood species. © 2017 2017The TheAuthors. Authors.Published Published Elsevier B.V. © byby Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the XIIth International Symposium «Intelligent Systems». Peer-review under responsibility of the scientific committee of the XIIth International Symposium “Intelligent Systems” Keywords: Hamming neural network; identification.
1. Introduction Classification of remote sensing data, as well as the identification of objects of natural origin for the purpose of carrying out qualitative and quantitative multi-temporal analysis plays important role in monitoring the state of the ecosystem and her individual classes. The most accurate indicators of classification of natural objects have methods of machine learning, but the maximum classification accuracy rates inherent in neural networks methods1,2. To carry out this study used a method Hamming neural network. Using this method justified for several reasons. At first, the calculation of the minimum Hamming distance reduces the probability of classification error input to the first neural network computational layer 3. At second, the implementation of a two-level associative memory to speed up the
* Corresponding author. E-mail address:
[email protected]
1877-0509 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the XIIth International Symposium “Intelligent Systems” doi:10.1016/j.procs.2017.01.126
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process of classification of a large number of naturally occurring objects4,5. At third, the possibility of implementation and use of a large number of training samples of reference images of natural objects6. The relevance of using Hamming neural network method for the classification of natural objects of the Southern Urals is to identify a large number of classes, consisting of coniferous and deciduous trees, as well as areas where there are two or more species of trees, water features, which include rivers, lakes, marshes, soil covers, including black soils, alumina, and mixed areas of soils, as well as the identification of areas burnt as a result of grass-roots and crown fires. It is worth noting that the use of Hamming neural network is urgency to identify areas where illegal logging can be carried out. The advantage of using the classification method based at the Hamming neural network method is an associative memory of successful implementation, relatively small volume calculations, as well as the economical use of hardware resources7. 2. Hamming neural network classification Classification of natural objects using Hamming neural network method is performed by comparing the satellite image supplied to the input and the reference image, and then calculates the Hamming distance. Accordingly, the lower the Hamming distance, the better the result of satellite image classification, which is expressed in the formula: H ( x, y )
¦ | xi yi | ¦ ( xi yi ) 2 i
(1)
i
Hamming neural network method is to decide which image of the reference vector closest to the input vector. The decision in the Hamming neural network is taken recurrently layer. In recurrently layer is present on one neuron for each reference image. Next is activated only one output of the neural network, which corresponds to the reference image8. Idea of using a Hamming neural network method for natural objects classification and identifying areas burned by forest fires and illegal logging conclude in the calculating Hamming distance in the process of classification of satellite images. The input data to the neural network of the Hamming served in the form of a matrix, which determines the number of neurons in the input network. In the case of the classification of satellite images, this feature allows most accurately identify each class. Hamming neural network consists of two layers, its structure is shown in Figure 1.
Fig. 1. Structure of the Hamming neural network.
Hamming neural network is composed of two layers of processing. The first layer is a neural network with direct links, in which the Hamming distance is calculated by comparing the reference image and supplied to the space image input. The second layer is a modified Hopfield neural network. The dimension m of the first layer is
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determined by the number of reference images (classes). The dimension m of the second layer is equal to the dimension of the first layer of the neural network. Neurons have a first layer on n synapses which are connected to the inputs X1 ... Xm. The neurons of the second layer are interconnected negative feedback synaptic connections. The only synapse with positive feedback for each neuron is connected to its axon. Adapting a mathematical model of Hamming neural network method is implement the use of vectors of reference images of different dimensions that are stored in the neural network itself. Given the fact that you are using satellite images with different spatial resolution, the number of values in the vectors of reference images is significantly different for the classification of objects of natural origin in the Southern Urals. The vectors of the reference master image of the same area obtained on the basis of satellite images Landsat 8 OLI / TIRS and Sentinel- 2A, the length will vary significantly due to the fact that the sensor Landsat 8 OLI / TIRS has a spatial resolution of 30 meters per pixel, and sensor Sentinel- 2A produce shooting with a spatial resolution of 10 meters per pixel. Therefore, if the vector of the reference image is made on the basis of satellite image obtained from the sensor Landsat 8 OLI / TIRS has 15 values, the vector of the reference image made on the basis of satellite image obtained Sentinel- 2A sensor has 135 values. The dimensions of the vectors differ nine times. The problem of classification of objects of natural origin in the Southern Urals, using reference images of the vectors of different dimensions made on the basis of satellite images with different spatial resolution implements an algorithm based at the Hamming neural network method. In the initialization step the first layer weights and the activation threshold value assigned the following functions:
wik
xik 2
,i
, n 1, k
0,
0,
, m 1,
(2)
where x is a memorable way, I is the corresponding components of the vector x; j is an image number; n is a dimension vector x; m is a number of stored images Tk
n 2
,k
, m 1,
0,
(3)
where Tk is a threshold activation function functioning Hamming Neural Network algorithm is as follows: 1. The input is pre-processed vector data, derived from satellite imagery. X
{ xi i
, n} .
0,
(4)
Further, the state of neurons is calculated Hamming layer (the first layer) (1)
yj
(1)
sj
n 1 w x 0 ij i
¦i
Tj , j
0,
, m 1 .
(5)
Thereafter, the obtained values are used to initialize the second layer of axons: (2)
yj
(1)
yj , j
0,
, m 1 .
(6)
3. At the second phase of the neural network are calculated Hamming new states of neurons of the second layer: (2)
s j ( p 1)
m 1 (2) y , 0 k
y j ( p) H ¦ k
k z j, j
and the values of the second layer of axons:
0,
, m 1 .
(7)
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(2)
y j ( p 1)
(2) f « s j ( p 1) » , j ¬ ¼
0,
, m 1 .
(8)
Activation function ݂ is given by the threshold, and, the F value should be large enough to ensure that any possible argument values do not lead to saturation. 3. At the third step is checked to change the output of the second layer in the last iteration. If changes have occurred, then the transition to the second stage of the neural network operation algorithm Hamming. If there is no change, the procedure ends. From the description of operation of the Hamming neural network algorithm seen that the role of the first layer is required to select the data to be classified. Also, as in the Kohonen neural network and Hopfield neural network, in the Hamming neural network weights between the layers are presented in the form of matrices, denoted by ܹ. Hamming neural network is stable if its matrix is symmetric and has zeros on the main diagonal, or if wii w jj , and wii 0 for all i. Stability Hamming neural network is proved by means of Lyapunov function, the meaning of which is always in a decreasing function when changing the state of the neural network. Ultimately, this function reaches a minimum, and stops changing, it ensures the stability of the Hamming neural network. The Lyapunov function is expressed in the formula: E
1 2
¦ i ¦ j wij OUTi OUT j ¦ j I j OUT j ¦ j T j OUT j .
(9)
where E is an artificial energy networks; the weight of the output neuron i to input neuron j; the output neuron j;external input neuron j; the threshold of neuron j. The change in energy E, due to changes in the state j- neuron is calculated as follows: GE
ª ( w OUTi ) I j T j º G OUT j «¬ ¦ i z j ij »¼
ª¬ NET j T j º¼ G OUT j .
(10)
If the value of NET neuron j greater than the threshold, then the expression in the brackets will be positive. From the equations (9), (10) that the output of neuron j varies in the positive direction or remains unchanged, which means that it can only have a positive value or be zero. Ɂ ܧvalue must be negative. This implies energy Hamming neural network must either decrease or remain unchanged. If the value of NET is below the threshold, then the value of G OUT j takes only negative values or zero. Consequently, the energy Hamming neural network decreases, or remains unchanged. If the value of NET is equal to the threshold, the value is zero and the energy of Hamming neural network remains unchanged. Due to the continuous striving to reduce the energy needed to achieve the minimum and stop changes. Therefore, this fact proves the stability of the Hamming neural network. Symmetry Hamming neural network is an important condition for stability. To optimize the classification of a naturally occurring process objects in the Southern Urals, using Hamming neural network method chosen technique of using associative memory, the essence of which is to maximize the reference line and fed to the input of the neural network image. Of course, when using a large number of standard classes and input data in the form of vector data, derived from satellite images, there is a problem of incorrect associations of reference and input images, but in order to avoid this problem in the process of classification of natural origin of objects, use a two-tier structure, which consists of untied Hamming memory and network decision. Unleashed associative memory calculates the Hamming distance by dividing an input vector into disjoint modules or local window and performs a full Hamming associative memory on each module independently. Unleashed Hamming associative memory works as follows: the key memory is divided into the same images as reference vectors, then the Hamming distance is calculated and displayed the closest matching template.
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In the case of a two-level Hamming associative memory, there are many different topologies, the possible location for the activation of the local memory points. For example, the activation point of the local memory may be arranged in rows, columns, or staggered. Each fragment Hamming local memory calculates the Hamming distance for the local area, and then for all the classified satellite image. One of the obvious advantages unleashed Hamming memory is the search speed and computation. Since all fragments can perform their computations in parallel, accelerating the computation time can be achieved by allocating the CPU to each fragment. At the level of decision-making, each piece of local memory Hamming calculates the closest corresponding standard and sends the index to the level of the best examples of a network decision-making. Decision analyzes network performance of all fragments of local memory and calculates the one best index, and then insert it into the fundamental memory. Because the solution uses the network all the pieces to put in the memory is fundamental, it is said that the problem with the associative memory error in the Hamming neural network is eliminated. The implementation of the two-level associative memory algorithm classification of objects of natural origin, based on the basis of the method of Hamming neural network helps to reduce the amount of computing processes in the second layer of the neural network. Accordingly, such an associative memory to speed up the process of classification of objects of natural origin in the Southern Urals, based on the method of Hamming neural network. In the process of implementing a two-tier Hamming neural associative memory networks, the optimization of this method of classification, which is the modification of the first layer of the neural network itself. The implementation of the two-level associative memory in the first layer of the neural network gives the Hamming method of classifying two advantages: 1. Improving the accuracy of the classification, which is realized at the expense of double-data processing input calculating the least Hamming distance, which implements module unleashed memory and network decisionmaking, in which the selected input with the smallest Hamming distance for further processing in the second layer of the neural network. 2. Increase the input data rate through the use of parallel computing at the level of memory and launched a network decision-making. Based on the fact that the neural network Hamming, on the basis of which the developed algorithm of classification of objects of natural origin in the Southern Urals, has a large number of training samples that are based on vector derived from the processing of satellite images, the best was the choice of methods of teaching the neural network without a teacher, the meaning of which is to use a large number of case studies. The process of learning is reduced to the adjustment to the weighting factors. The method of training the neural network of the Hamming method involves the use of a signal Hebb, the essence of which is to strengthen the relationship between the excited neurons, in this case, the Hamming weight of the neural network changes by the following rule: wij (t 1)
[ n 1]
wij (t ) a yi
y nj .
(11)
where y[ n 1] is thee neuron output value of n-1 layer; y[ n ] is the output value of the neuron layer n; wij (t 1) and wij (t ) are the weight ratio of the synapse connecting the neurons in the iteration t; a- training speed ratio.
As is known, the network output neuron is the Hamming weighted sum of its inputs, which is expressed as follows: NET j
¦ i OUTi wij .
(12)
where NET j is an output NET of neuron j; OUTi is an output of neuron i; wij is a neuron weight i to the neuron j. To recognize more classes, Hamming neural network has undergone modification by introducing non-linearity in the transfer function of the neuron. Therefore, the neural network uses Hamming sigmoidal activation function and accordingly is trained on the signaling method Hebb. In this case, the Hebb's equation takes the following form:
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wij (t 1)
OUTi
wij (t ) OUTi OUT j , 1
1 exp( NETi )
F ( NETi ) ,
(13)
(14)
where wij (t ) is synapse signal from neuron i to neuron j, and at time t, OUTi is the output level of the presynaptic neuron, which is F ( NETi ) , OUT j is a postsynaptic neuron output level, which is equal to F ( NET ) . Using a sigmoidal activation function in the algorithm classification of objects of natural origin in the Southern Urals, based on the method of Hamming neural network allows to amplify weak signals and not be satisfied with the strong. Given that the classification is carried out using a vector that contain the values of the normalized difference vegetation index, it is the only way by which you can identify a large number of classes of naturally occurring objects, as well as identify areas burnt due to forest fires, which are carried out illegal deforestation, as continuous and random. For classification of objects of natural origin in the Southern Urals, the neural network method Hamming has been implemented in GRASS GIS as a module written in the Python programming language. 4. Method For the classification of objects of natural origin using the neural network method was used Hamming space image with a spatial resolution of 30 square meters per pixel, which has been obtained using the sensor OLI / TIRS satellite system Landsat 8 June 25, 2015 at 07:09:59, Path 165, Row 24. The criteria for selecting remote sensing data served cloud cover less than 30% and the seasonal time of recording. To create master images of training samples of coniferous and deciduous trees georeferenced map of plant resources was used, GPS data and satellite images satellite systems Landsat 7 ETM +, Landsat 8 OLI / TIRS, during the vegetation period 2012 - 2015. Using the information of plant resources maps, homogeneous and heterogeneous ranges of coniferous and deciduous trees were identified on satellite images. Total twelve homogeneous classes of tree species were identified: pine, larch, spruce, fir, oak, maple, birch, linden, aspen, alder, poplar, willow, and five of heterogeneous classes, including coniferous and broad-leaved species. To create master images of training samples of water bodies has been applied a combination of near-infrared, mid (shortwave) infrared and red channels of satellite images Satellite System Landsat 7 ETM +. This combination allows you to select channels polygons of water bodies due to the contrast with the land objects. Next classes were allocated rivers, lakes, wetlands and reservoirs. To create master images of training samples of objects devoid of vegetation, was used a combination of secondary (shortwave) infrared, near-infrared and red channels of satellite images Landsat 7 ETM +. Using this combination of channels allows you to clearly distinguish objects, devoid of vegetation on the vegetation classes of objects and bodies of water. classes of bare soil and mineral resources have been allocated. More complicated is the task of identifying illegal logging, for the reason that, as a rule, have the character of illegal logging and selectivity are held in mixed tiers of coniferous and broad-leaved trees. Otherwise, do not cut down the entire array, but only a certain breed. To create master images of training samples to detect illegal logging, GPS data were used. On satellite images was applied information in the form of georeferenced vector layer, which includes the coordinates by landfill sites, which were carried out illegal logging. To detect changes in tiers of coniferous and broad-leaved stands originally calculated a normalized index NDVI distributed according to the formula: NDVI
NIR RED NIR RED
,
where NIR is near infrared band.
(15)
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To calculate the normalized difference vegetation NDVI index is distributed according to the formula:
Fig. 2. The result of the natural objects classification with use the Hamming neural network method.
DNDVI
NDVI pre NDVI post ,
(16)
where NDVIpre is the value of the normalized vegetation index prior before logging, NDVIpost is the normalized value of the distributed index after logging. To create a standard training samples territories burned due to forest fires used multi-temporal satellite images satellite systems Landsat 7 ETM +, Landsat 8 OLI / TIRS, Terra ASTER. To reveal to unclassified satellite images burnt areas due to forest fires, it is necessary to calculate the short-growing SWVI index according to the formula: SWVI
NIR SWIR NIR SWIR
,
(17)
where NIR is near infrared band, SWIR is shortwave infrared band. Then calculate the difference shortwave SWVI vegetation index according to the formula: DSWVI
SWVI pre SWVI post ,
(18)
where SWVIpre is the value of short-vegetation index before the fire, SWVIpost is the value of short-vegetation index after the fire. Depending on the type of fire and the time since the satellite imagery fire, vegetation indexes NDVI values DSWVI and lie in a positive range of 0.1 to 0.5. After all the classes were obtained data on satellite images using a priori information about the naturally occurring sites of burned areas and on sites in the Southern Urals, where carried out illegal logging, the next step, based on satellite images, which were used to retrieving information about classes of naturally occurring objects burned areas and areas on which conducted illegal logging are the vectors of reference matrices. 5. Conclusion Hamming neural network method has been implemented in a software module GRASS GIS and used to classify objects of natural origin in the Southern Urals. To determine the accuracy of the classification Kappa factor was
O.I. Khristodulo et al. / Procedia Computer Science 103 (2017) 388 – 395
used. As a result, the classification accuracy was 98.2%. Also, the classification results were evaluated by comparing the use of a priori information collected using GPS field data. In the classified image correctly identified classes of coniferous and deciduous vegetation, water bodies, area burnt due to forest fires, as well as areas of bare soil (Figure 2). It is worth noting the use of the method of Hamming neural network for natural objects classification, actual use of GIS in practice, this method allows to identify as precisely as possible the greatest number of classes. A feature of using this method is also a minimization of classification errors by processing the input data double. The research results presented in the paper are partially supported by the grant 15-08-01758 – A "Methodological and procedural framework of the technological security analysis under uncertainty of the management facilities conditions". References 1. Khristodulo O.I., Makhmutov A.A.. Applying neural network classification methods for natural objects recognition. Proceeding of the 1-st International conference, DSPTech’2015 . Ufa State Aviation Technical University 2015; 1: 81-4. 2. Makhmutov A.A., Khristodulo O.I., Gatiyatullin D.I., Musin I.A. Estimation classification accuracy of algorithms based at artificial intelligence methods for remote sensing data of South Ural area Proceedings of the 4-th international conference ITIDS’2016. Ufa State Aviation Technical University 2016; 2: 19-24. 3. Hamming R. Coding and Information Theory. Prentice-Hall Englewood Cliffs, NJ; 1968. 4. Ikeda N., Watta P., Artiklar M., Hassoun M.H. A two-level hamming network for high performance associative memory. Neural Networks Journal, 2001; 14: 1189-200. 5. Chou P. The capacity of the kanerva associative memory. IEEE Transactions on Information Theory 1989; 35, 2: 281-98
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