CLASSIFICATION OF FOREST AND NON-FOREST AREAS

R.Sathiyavani* Prof V.Srinivasan.

Department of Computer Science, Annamalai University

Abstract

The study investigates the performance of image classifiers for forest and non-forest area classification. Remote sensing image classification is one of the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. We are experimenting with both supervised and unsupervised classification. Here we compare the different classification methods and their performances. Specially tested are performances of Maximum Likelihood classifier, Minimum Distance classifier, Parallelopiped classifier based on Landsat7 ETM+ spectral data and produced high accuracies of more than 75% with limited input information. Of the classified images, the maximum likelihood method is found to be more applicable and reliable for the satellite image classification purposes and the Parallelopiped method is found to give the least reliable results compared to the other methods.

Keywords: Forest classification; Maximum Likelihood Classifier; Minimum Distance; Parallelopiped Classification accuracy.

1. INTRODUCTION:

Remote sensing, particularly satellites offer an immense source of data for studying spatial and temporal variability of the environmental parameters. Image classification is an important part of the remote sensing, image analysis and pattern recognition. In some instances, the classification itself may be the object of the analysis. For example, classification of land use from remotely sensed data produces a map like image as the final product of the analysis[1]. The image classification therefore forms an important tool for examination of the digital images.

Forest classification has evolved from the initial identification of forested areas to the determination of variation in species diversity. Initially, efforts at classifying forests were motivated by management objectives, i.e. to safeguard supplies of timber and other forest products, and to provide environmental services, particularly the protection of fragile mountain catchments areas[2]. Over the years, however, nature conservation, recreation, research and education have presented additional objectives. With the growing interest in developing conservation strategies for species diversity, there is a need to consider the variation within the vegetation cover (including non-forest areas).Deforestation is the major problem existing in the world now. Therefore, classifying the forest and non-forest areas help us to evaluate the areas that turn to non-forest.

2. SATELLITE IMAGE CLASSIFICATION

Image classification in the field of remote sensing, is the process of assigning pixels or the basic units of an image to classes. It is likely to assemble groups of identical pixels found in

remotely sensed data, into classes that match the informational categories of user interest by comparing pixels to one another and to those of known identity. This categorized data may then be used to produce thematic maps of the land cover present in an image. Studies[3] indicate that spectral information is an effective means of achieving this goal. The spectral pattern present within the data for each pixel is used as the numerical basis for categorization. . A Multi-spectral image is one that captures image data at specific frequencies across the electromagnetic spectrum. The wavelengths may be separated by filters or by the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, such as infrared. Multi-spectral imaging can allow extraction of additional information that the human eye fails to capture with its receptors for red, green and blue. It was originally developed for space-based imaging. Several methods of image classification exist. Two main classification methods are Supervised Classification and Unsupervised Classification [4]. Unsupervised classification is a method which examines a large number of unknown pixels and divides into a number of classes based on natural groupings present in the image values. Unsupervised classification does not require analyst-specified training data. This classification is becoming increasingly popular in agencies involved in long term GIS database maintenance. Supervised classification of remote-sensing images has been widely used as a powerful means to extract various kinds of information concerning the earth environment. The objective of supervised classification in remote sensing is to identify and partition the pixels comprising the noisy image of an area according to its class (e.g. forest and non-forest), with the parameters in the model for pixel values estimated from training samples (ground

truths).Supervised classification is the procedure most often used for quantitative analysis of remote sensing image data. It rests upon using suitable algorithms to label the pixels in an image as representing particular ground cover types, or classes. A variety of algorithms is available for this, ranging from those based upon probability distribution models for the classes of interest to those in which the multispectral space is partitioned into class specific regions using optimally located surfaces.

For nearly a century, forest and national park agencies have been engaged in mapping forest areas in order to provide managers information and monitor the condition of ecosystems over time. There have been attempts to improve the accuracy of the forest maps. But this is an attempt to evaluate the best of the classifier that suits for forest and non-forest area classification. In this study three supervised classification methods were compared i.e. Maximum Likelihood classifier (MLC), Minimum Distance classifier, Parallelopiped classifier. These classification methods were chosen because MLC has been noted to have a more robust theoretical basis and higher accuracies[5], whereas Minimum distance classifier and parallelopided classifier are found to be very simple to implement with less complexity.

3. STUDY AREA

Rio de Janeiro lies on a strip of Brazil's Atlantic coast, close to the Tropic of Capricorn, where the shoreline is oriented east–west. Facing largely south, the city was founded on an inlet of this stretch of the coast, Guanabara Bay and its entrance is marked by a point of land called Sugar Loaf– a "calling card" of the city.It lies in the 22°54′30″S 43°11′47″W coordinates[6].

This region retains good quality forest, including elfin forest, and has the most continuous well-preserved remnant of Atlantic forest in the state. Surveys occurred at four locations, each of which had montane forest, and usually elfin forest. Araras Biological Reserve is a state conservation unit located between Serra dos Órgãos National Park and Serra do Tinguá Biological Reserve Fazenda Itatiba is within Três Picos State Park. Both sites are part of the Montane Central Region, covered mainly by Montane Atlantic Forest. The

Fazenda Boa Esperança site is between Três Picos and Desengano State Park, but more than 30 km from both of these protected areas. The Desengano State Park is located more than 50 km from all other sites, with the forest being currently isolated due to deforestation in surrounding areas. All of these sites are part of a once continuous forest with one of the highest biodiversity levels in the world including many threatened species[7].

The Tijuca Forest is a mountainous hand-planted rainforest in the city of Rio de Janeiro, Brazil. It is the world's largest urban forest, covering some 32km² (12.4mi²). The Tijuca Forest is home to hundreds of species of plants and wildlife, many threatened by extinction, found only in the Atlantic Rainforest.

4. DATA PREPARATION

In this research, we have made use of land cover images obtained from remote sensing for experimentation. Landsat 7 is equipped with an enhanced Thematic Mapper (ETM+) as in fig.3 has been used. Landsat 7’s ETM+ is different from thematic mappers because it offers following features.1. A panchromatic band with 15m spatial resolution. 2. On-board, full aperture, 5% absolute radiometric calibration. 3. A thermal IR channel with 60m spatial resolution. 4. An on-board data recorder.

The data set consists of 800*625 pixels and covers Rio de Janeiro of Brazil[8] shown in Fig.1. The advantage of using this dataset is the availability of the referenced image produced from field survey, which is used for the accuracy purpose. The original remote sensing image in false colors with RGB: 432 with its characteristics Table 1ist shown in Table.1. A ground truth image (reference image) is generated by field study campaign as in Fig. 2. Random sampling is carried out to select the pixels for training and testing the classifiers.

Table 1.characteristics of Landsat7+ETM

Sensor:L7ETM+AcquisitionDate:February28,2000Path/Row:217/76Lat/Long:-22.904/-43.210

Fig. 1 Map of South America Fig. 2 Rio de Janeiro

Fig. 3 Satellite image of Rio de Janeiro

4. METHODOLOGY

The main aim of the study is to evaluate the performance of the different classification algorithms for forest and non-forest areas classification using the multispectral data. Irrespective of the classifier used there are some basic steps used in supervised image classification.

1. Decide the set of ground cover types into which the image is to be segmented. These are the information classes like forest, non-forest, water etc.

2. Choose representative or prototype pixels from each of the desired set of classes. These pixels are said to form training data. Training sets for each class can be established using site visits, maps, air photographs or even photo interpretation of a color composite product formed from the image data. Often the training pixels for a given class will lie in a common region enclosed by a border. That region is then often called a training field.

3. Use the training data to estimate the parameters of the particular classifier algorithm to be used; these parameters will be

the properties of the probability model used or will be equations that define partitions in the multispectral space. The set of parameters for a given class is sometimes called the signature of that class.

4. Using the trained classifier, label or classify every pixel in the image into one of the desired ground cover types (information classes). Here the whole image segment of interest is typically classified. Whereas training in Step 2 may have required the user to identify perhaps 1% of the image pixels by other means, the computer will label the rest by classification.

5. Produce tabular summaries or thematic (class) maps which summarize the results of the classification.

6. Assess the accuracy of the final product using a labelled testing data set.

4.1 DEFINING THE TRAINING SAMPLES

Supervised classification is much more accurate for mapping classes, but depends heavily on the cognition and skills of the image specialist. The strategy is simple: the specialist must recognize conventional classes (real and familiar) or meaningful (but somewhat artificial) classes in a scene from prior knowledge, such as personal experience with what's present in the scene, or more generally, the region it's located in, by experience with thematic maps, or by on-site visits. This familiarity allows the individual(s) making the classification to choose and set up discrete classes (thus supervising the selection) and then, assign them category names. As a rule, the classifying person also locates specific training samples on the image - either a print or a monitor display - to identify the classes. The resulting Training samples are areas representing each known land cover category that appear fairly homogeneous on the image (as determined by similarity in tone or color within shapes delineating the category). In the

computer display one must locate these samples and circumscribe them with polygonal boundaries drawn using the computer mouse[9]. More than one polygon is usually drawn for any class. The quality of a supervised classification [10] depends on the quality of the training sites.

4.2 SIGNATURE EXTRACTION

After the training site areas have been digitized, the next step is to create statistical characterizations of each information. For each class thus outlined, mean values ,standard deviation and covariance of the selected area for each band used to classify them are calculated from all the pixels enclosed in each site.

4.2.1. Estimation of MEAN for Classes:

The true values of the mean and covariance matrix are not known and must be estimated from training samples. The mean is typically estimated by the sample mean

where Xi,j the sample j from class i.

Ni the number of training samples from class i

mi the sample mean

4.2.2 Estimation of Covariance Matrix for Classes:

The covariance matrix is typically estimated by the sample covariance matrix

Where i the sample covariance matrix

4.3 CLASSIFICATION METHODS

4.3.1 Maximum Likelihood Classifier

Maximum likelihood Classification is a statistical decision criterion to assist in the classification of overlapping signatures; pixels are assigned to the class of highest probability. The maximum likelihood classifier is considered to give more accurate results than parallelepiped classification however it is much slower due to extra computations. We put the word `accurate' in quotes because this assumes that classes in the input data have a Gaussian distribution and that signatures were well selected; this is not always a safe

assumption.[11] Multivariate normal statistical theory describes the probability that an observation X will occur, given that it belongs to a class k, as the following function

Where k Parametric mean vector associated with the k th class.

X p-dimensional random vector

k covariance matrix associated with the k th class.

k(Xi) Probability density value associated with observation Xi as

as evaluated for class k..

The quantitative product

can be thought of as a squared distance function which measures the distance between the observation and the class mean as scaled and corrected for mean and covariance of the class. It can be shown that this expression is an X 2 variate with p degrees of freedom.

As applied in a maximum likelihood decision rule, expression (1) allows the calculation of the probability that an observation is a member of each of k classes. The individual is then assigned to the class for which the probability value is greatest. In an operational context, we substitute observed means, variances, and covariances and use the log form of expression (1)

Where Dk p by p dispersion matrix associated with a sample of observations belonging to the k th class

Since the log of the probability is a monotonic increasing function of the probability, the decision can be made by comparing values for each class as calculated from the right hand side of this equation

4.3.2 Minimum distance Classification

Minimum distance classifier classifies image data on a database file using a set of 256 possible class signature segments as specified by signature parameter. Each segment specified in

signature, for example, stores signature data pertaining to a particular class. Only the mean vector in each class signature segment is used. Other data, such as standard deviations and covariance matrices, are ignored (though the maximum likelihood classifier uses this)[13].

The result of the classification is a theme map directed to a specified database image channel. A theme map encodes each class with a unique gray level. The gray-level value used to encode a class is specified when the class signature is created. If the theme map is later transferred to the display, then a pseudo-color table should be loaded so that each class is represented by a different color.

The equation used by minimum Euclidian distance classifier defined by the following equation[12]:

Gi(X) = (X-Ui)T * (X-Ui)

= SUM [(xj-uj) **2] for j = 1 to d.

Gi(X) is the result for class i on pixel X

T indicates transposition of the elements in brackets

d is the number of pixels in the classification

X=(x1,…, xd) is the (d by 1) pixel vector of grey-levels

Ui=(u1,...,ud) is the (d by 1) mean vector for class i

J is the subscript of jth element of a vector

SUM[]is the total of elements inside brackets

The distances between the pixel to be classified and each class centre are compared. The pixel is assigned to the class whose centre is the closest to the pixel.

If for all i not equal j, Gj(X) < Gi(X), then X is classified as j.

Parallelepiped Classification Algorithm

This is a widely used decision rule based on simple Boolean “and/or” logic.

Training data in n spectral bands are used in performing the classification.

Brightness values from each pixel of the multispectral imagery are used to produce an n-dimensional mean vector, Mc = (μc1, μc2, μc3, ... μcn) with μck being the mean value of the training data obtained for class c in band k out of m possible classes, as previously defined. Sck is the standard deviation of the training data class c of band k out of m possible classes.

The decision boundaries form an n-dimensional parallelepiped in feature space. If the pixel value lies above the lower threshold and below the high threshold for all n bands evaluated, it is

assigned to an unclassified category. Although it is only possible to analyze visually up to three dimensions, it is possible to create an n-dimensional parallelepiped for classification purposes. The parallelepiped algorithm is a computationally efficient method of classifying remote sensor at a [14]. Unfortunately, because some parallelepipeds overlap, it is possible that an unknown candidate pixel might satisfy the criteria of more than one class. In such cases it is usually assigned to the first class for which it meets all criteria. A more elegant solution is to take this pixel that can be assigned to more than one class and use a minimum distance to means decision rule to assign it to just one class.

Classification Accuracy Assessment

Quantitatively assessing classification accuracy requires the collection of some in situ data or a priori knowledge about some parts of the terrain which can then be compared with the remote sensing derived classification map. Thus to assess classification accuracy it is necessary to compare two classification maps 1) the remote sensing derived map, and 2) assumed true map (in fact it may contain some error). The assumed true map may be derived from in situ investigation or quite often from the interpretation of remotely sensed data obtained at a larger scale or higher resolution.[15]

Classification Error Matrix

One of the most common means of expressing classification accuracy is the preparation of classification error matrix sometimes called confusion or a contingency table. Error matrices compare on a category by category basis, the relationship between known reference data (ground truth) and the corresponding results of an automated classification. Such matrices are square, with the number of rows and columns equal to the number of categories whose classification accuracy is being assessed. error matrix that an image analyst has prepared to determine how well a Classification has categorized a representative subset of pixels used in the training process of a supervised classification. This matrix stems from classifying the sampled training set pixels and listing the known cover types used for training (columns) versus the Pixels actually classified into each land cover category by the classifier (rows). An error matrix expresses several characteristics about classification performance. For example, one can study the various classification errors of omission (exclusion) and commission (inclusion). Several other measures for e.g. the overall accuracy of classification can be computed from the error matrix. It is determined by dividing the total number correctly classified pixels (sum of elements along the major diagonal) by the total number of reference pixels. Likewise, the accuracies of individual categories can be calculated by dividing the number of correctly classified pixels

in each category by either the total number of pixels in the corresponding rows or column. Producers accuracy which indicates how well the training sets pixels of a given cover type are classified can be determined by dividing the number of correctly classified pixels in each category by number of training sets used for that category (column total). Users accuracy is computed by dividing the number of correctly classified pixels in each category by the total number of pixels that were classified in that category (row total).

Kappa coefficient

Kappa analysis is a discrete multivariate technique for accuracy assessment

Kappa analysis yields a Khat statistic that is the measure of agreement of accuracy. The Khat statistic is computed as

Where r is the number of rows in the matrix xii is the number of observations in row i and column i, and xi+ and x+i are the marginal totals for the row i and column i respectively and N is the total number of observations.

A study of the performance of various classifiers based on the overall accuracy, kappa coefficient, and confusion matrix and it is shown in table 2 and 3 is made. It is observed that Maximum Likelihood classification method is determined to be the most accurate. One of the reasons is it filters out shadows and also it classifies the highly varied clusters.

Table.2 Maximum Classification Error matrix (Pixels)

CLASS CLASS1 CLASS2 CLASS3 TOTAL

Unclassified 136 5 2 143

Class1[Red] 65367 1 0 65368

Class2[Blue] 0 1514 0 1514

Class3[Green] 0 0 1022 1022

Total 65503 1520 1024 68047

Table 3 Maximum likelihood Classification Error matrix (Percentage)

CLASS CLASS1 CLASS2 CLASS3 TOTAL

Unclassified 0.21 0.33 0.2 0.21

Class1[Red] 99.79 0.07 0 96.06

Class2[Blue] 0 99.61 0 2.22

Class3[Green] 0 0 99.8 1.5

Total 100 100.01 100 100

Producer’s Accuracy and Users Accuracy

Red(non-forest)= 65367/65503 = 99.8% Red= 65367/65368 = 99.9%

Green(forest) = 1514/1520 = 99.6% Green= 1514/1514 = 100%

Blue (water) = 1022/1024 = 99.8% water = 1022/1022 = 100%

Overall accuracy = (65367+ 1514 + 1022)/68047= 99.7%

Kappa Coefficient =0.9716.

The error matrix shows the accuracy of Maximum Likelihood classification method using the image provided. Similarly the error matrices for other classification were found out. The minimum distance classifier is found to be the least accurate with the lowest accuracies. The output of the classification is shown in figure [Fig. 4-6]. Overall, the Maximum Likelihood classifier shows the highest accuracy assessment for this particular area.

6. Conclusion

In this paper we have compared the performance of various classifiers and found that the Maximum Likelihood classifier outperforms other classifiers. This accurate but simple classifier shows the importance of considering the data set - classifier relationship for successful image classification. Further studies are required to improve the use of classifiers to increase the applicability of such methods. There is a need to develop new work to compare the supervised classifiers with unsupervised classifiers and identify the pros and cons of among them.

Reference:

[1] Campbell, “Introduction to Remote Sensing”, GE-21,pp.383-392,2002.

[2] Grace Nangendo , Andrew K. Skidmore b, Henk van Oosten , “Mapping East African tropical forests and woodlands — A comparison of classifiers”, ISPRS Journal of Photogrammetry & Remote Sensing 61 ,pp.393–404, 2007

[3] Vieira, I.C.G., Almeida, A.S.d., Davidson, E.A., Stone, T.A., Carvalho, C.J.R.d., Guerrero, J.B., “Classifying successional forests using Landsat spectral properties and ecological characteristicsin eastern Amazonia”,Remote Sensing of Environment 87 (4), 470–481,2003.

[4] Sudhir Gupta, “Exploring The Utility Of Moderate Resolution Time Series Remotely Sensed Data For Land Use/Cover Classification”, International Institute of Information Technology, Hyderabad, INDIA, December 2009.

[5] M. Heinl, J. Walde, G. Tappeiner, U. Tappeiner ,” Classifiers vs. input variables—The drivers in image classification for land cover mapping”, International Journal of Applied Earth Observation and Geoinformation 11 , pp.423–430, 2009

[6] www.wikipedia.com

[7] Maria Alice S. Alves ,Clinton N. Jenkins,Stuart L. Pimm ,Alline Storni,Marcos A. Raposo ,M. de L. Brooke,Grant Harris ,Andy Foster, “LISTS OF SPECIES Birds, Montane forest, State of Rio de Janeiro, Southeastern Brazil”, ISSN: 1809-127X, 289–299, 2009.

[8] http://landsat.usgs.gov http://landsat.usgs.gov/gallery_view.php?category=nocategory&thesort=mainTitle

[9] http://rst.gsfc.nasa.gov/Sect1

[10] C. Palaniswami, A. K. Upadhyay and H. P. Maheswarappa, "Spectral mixture analysis for subpixel classification of coconut", Current Science, Vol. 91, No. 12, pp. 1706 -1711, 25 December 2006.

Alan H. Strahler, “The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data”, Remote Sensing Environment10,135-163 (1980).

[11] http://www.pcigeomatics.com/cgi-bin/ pcihlp/MINDIS

[12] Aykut Akgun, A.Husnu Eronatb and Necdet Turka, “Comparing different satellite image classification methods: An application in Ayvalik district, western Turkey”,vol.7,678-697,2009.

[13] K Perumal and R Bhaskaran, “Supervised classification performance of multispectral images”, Journal of computing, vol. 2, issue 2, 151-167, February 2010

[14] Minakshi Kumar, “Digital Image Processing”, Photogrammetry and Remote Sensing Division, Indian Institute of Remote Sensing, Dehra Dun.

Fig-4 Parallelopiped classification

Fig-5 maximum Likelihood classification

Fig-6 Minimum distance classification