Land-cover classification using SAR and MS image

with ANN classifiers

S N Omkar1, Aditi Kanjolia2 , Sandesh C3 and Ashoka Vanjare4

ABSTARCT: In this work, gray-level co-occurrence matrices (GLCM) have been used to quantitatively evaluate statistical

textural parameters for a SAR image and to generate a filtered image to feed to ANNs for classification for land cover. Prior

to performing the textural analysis, an adaptive filter was applied to reduce the effect of radar-system-generated coherent

speckle to produces an image approximating local tone while preserving edge definition. A feature set was than chosen

that best classifies the SAR image into the aimed classes. The features are selected based on their discrimination ability and

classification accuracy. And at last, the three ANNs used were compared using the image formed by the chosen features in

combination.

Index Terms: GLCM, SAR, texture, texture measures, speckle, adaptive filter; classification, multispectral, artificial neural

networks.

Section 1. Introduction

Land cover refers to the physical and biological cover at the surface of the earth, including water, vegetation, bare ground, man-made structures, etc. Land cover information acts as important piece of information for assessment of land utilization and resources. It can indeed play a significant role in planning for optimized usage of the resources in hand. Due to its technological robustness and cost-effectiveness, remote sensing has been increasingly used to derive land cover information through either manual interpretation or automated classification. The latter is more desirable but can be less effective for classifying heterogeneous landscapes. Artificial neural networks are commonly conceived to have the capability of improving automated classification accuracy due to their distributed structure and strong capability of handling complex phenomena.Many experiments have shown that MLP neural networks are more accurate for land cover classification than traditional statistical methods (Bischof et al., 1992; Civco, 1993; Serpico et al., 1996; Chen et al., 1997) The characteristics of data acquired by optical and synthetic aperture radar (SAR) sensors greatly differ. Multispectral satellites such as Landsat provide information on the energy scattered and radiated by the Earth’s surface in different wavelengths, from the visible to the thermal infrared, providing the ability to discriminate between different land cover classes such as vegetated areas, water surfaces, and urban centers. SAR sensors such as the European Remote Sensing satellites (ERS) ½ provide measurements in amplitude and phase related to the interaction of the Earth’s surface with microwaves. These acquisitions (C-band) are characterized by high returns from buildings in urban areas and low and very low values from vegetated areas and water surfaces, respectively. Within residential areas, further discrimination is achievable because the low-density areas are generally characterized by lower backscattering, given the wide streets and the presence of trees. This means that SAR sensors provide information that may not be obtained from optical sensors, and therefore, data fusion potentially provides improved results in the classification process compared to the conventional single-source classification results [27]. Consideration of the visual interpretation of radar images reveals that the intrinsic spatial variability, or "texture," of the image, beyond that caused by speckle, is a valuable feature in discriminating among different land-use types. Texture may, in fact, be more useful than image tone in interpreting radar images. Many works says that texture filtering can improve the classification of SAR images. [1][2][3]. Although no precise definition of texture exists, certain concepts of texture can be defined [4].Texture involves the spatial distribution of gray levels in a local region. It contains important information about the structural arrangement of surfaces and their relationship to their neighboring surfaces. Some studies [5][6][7][8][9] have indicated that classification based on texture might be more robust than classification based on gray values alone. F. Pacifi, et al.,[28] introduced a novel method to use a combination of Multispectral and SAR data to achieve improved results in the classification process compared to theconventional single-source classification results, with the usage of Artificial Neural Network Classifier. This paper proposes a similar idea, to use a more robust information in the form of SAR

1 Chief Research Scientist, Indian Institute of Science ; contact : omkar@aero.iisc.ernet.in 2 Student , IIT Indore ; contact : ee1200202@iiti.ac.in 3 Student, IIT Kharagpur ; contact : sandeshc@cse.iitkgp.ernet.in 4 Research Assistant, Indian Institute of Science ; contact : ashokavanjare@gmail.com

and MS data to produce a classification that is an improvement over the conventional statistical methods using ANN classifiers and compare the impact of each of those classifiers on the classification problem. The paper is organized as follows: An idea of the study area under consideration is presented in Section 2 , followed by a brief description about statistical characteristics of a Radar Image in Section 3. Section 4 is used to describe the methods used to process the SAR data and the outcomes in the due process is also presented. A brief description of the three Artificial Neural Network Classifiers used , viz. , the MLP(Multi-Layer Perceptron), RBF-NN(Radial Basis Function - Neural Network) and the recently developed complex-valued neural network technique CC-ELM(Circular Complex valued Extreme Learning Machine) , is presented in Section 5. This section is then followed by Section 6, where the preparation of the Data Set (Training and Testing) for the application of supervised methods, is presented along with the challenges faced in due course. Section 7 presents the performance evaluation of the various parameters of texture filtering, in order to settle for the parameters which are best for the improvement of the final classification of the full-image. Section 8 is dedicated to the qualitative analysis of the classified images generated from the three classifiers using the data set prepared form the results obtained in Section 7.Finally, Section 9 summarizes the main conclusions from this study.

Section 2.Study area and data:

The study area is urban region of Bangalore City (12° 58′ N, 77° 34′ E), located in the South-East region of Karnataka State

in Southern India. The images used in this paper were taken in the year 2014 and are given below.

Figure A. Figure B.

A.) Radar data: Our data set is a C-band (7.5-3.75 cm wavelength, 5.35 GHz) single-polarized RISAT-1 SAR data. The

polarization is HH (horizontal transmit, vertical received). The resolution of this image is approximately 18 X 18 m. Area =

1602.63 square kms.

B.) Multispectral data: A 30 m resolution, spectral data has been used for the purpose of classification of the same region.

Section 3. Statistical Characteristics of a Radar Image:

The statistical variations observed in a radar image of a distributed target are attributed to two sources: signal fading and

intrinsic scene variability. The coherent nature of SAR gives rise to pixel-to-pixel variations in image intensity (or a related

quantity) that accounts to coherent fading (or speckles). The spatial variability in the scattering properties of the ground

cells that constitute a target gives rise to an intrinsic scene texture.

Speckle in SAR images is a scattering phenomenon. Speckle appearing in synthetic aperture radar (SAR) images is due to

the coherent interference of waves reflected from many elementary scatterers. This effect causes a pixel-to-pixel variation

in intensities, and the variation manifests itself as a granular noise pattern in SAR images [10].

Speckle can be treated as a random process governed by signal fading and was considered to be statistically independent

of the textural variations associated with the spatial variations of the scattering properties of various visually "uniform"

distributed targets in the SAR image. Considering this the whole experimentation was done.

Section 4.SAR Data processing:

Speckle Filtering: First, speckles were removed using adaptive filters. One such approach was earlier used by some

authors. [11][12].Speckle reduction filters for the images that contain high frequency information such as edges and

texture, should be adaptive to the local texture information. These adaptive filters can smooth speckle in homogeneous

areas while preserving texture and high frequency information in heterogeneous areas. Numerous adaptive filters have

been proposed in the past and the few famous are the Lee [14], the Kuan [15], the Frost [16], and the modified Lee and

modified Frost filters proposed by Lopes et al. [17]. For that, we choose modified frost filter. Modified adaptive filters are

effective in preserving edges and texture in the images, ‘A Comparison of Digital Speckle Filters by Zhenghao Shi and KO B.

Fung’, proven this result [18]. Both modified lee and frost filters are equivalent for small window sizes. Modified Frost filter

was chosen for this work[17].

Parameters chosen: The size of the window should be related to the size of the objects, relative to the spatial resolution of

the scene. A 5 *5 seemed a good choice for the data. The filter was applied via Envi 4.8 software package, which gives us

this filter as an inbuilt-feature. The damping factor was taken to be 0.1 and coefficient of variation for homogeneous areas

(Cu) and for heterogeneous areas (Cmax), was taken as that for a single look image as 0.5230 and 1.7320 respectively,

following the deductions of[17].The speckle-filtered image is shown below.

Texture Filtering: Next, the speckle-filtered image was texture filtered, based on GLCM texture model [19].

GLCM texture model: In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second-order and higher-order statistics. The Gray Level Co-occurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. A GLCM is a matrix where the number of rows and columns is equal to the number of gray levels, in the image. Suppose an image to be analyzed is rectangular and has Nx resolution cells in the horizontal direction and Ny resolution cells in the vertical direction. Let the gray tone appearing in each resolution cells be quantized to Ng levels. Let Lx ={1,2,….,Nx} be the horizontal spatial domain, Ly ={1,2,..., Ny} be the vertical spatial domain and G ={1,2...,Ng} be the set of Ng quantized gray tones. The set Ly x Lx is the set of resolution cells of the image ordered by their row- column designations. The image I can be represented as a function which assigns some gray tone in G to each resolution cell or pair of coordinates in Ly x Lx , such that I: Ly x Lx --> G GLCM is a matrix of relative frequencies Pij with which two neighbouring resolution cells separated by distance d occur on the image, one with gray tone i and the other with gray tone j. This matrix is a function of the angular relationship between the neighbouring resolution cells as well as a function of the distance between them. Formally, for angles quantized to 45o interval the unnormalied frequencies are defined as follow:

Let I (k,l) = i, I (m,n) = j

where : # denotes the number of elements in the set. Note that these matrices are symmetric.

Textural features : They are computed from a co-occurrence matrix of relative frequencies of gray levels at neighbouring pixels. Many statistics may be derived from the GLCM. 8 of them which are commonly used have been

applied in this paper. These features are stated in the table according to their groups. Group 1 measures are for

smoothness ; group 2 for homogeneity and group 3 for general statstics.

Notations :

The texture measures have been defined below :

Texture measurement requires the choice of window size, quantization levels, displacement value, and orientation factors

for each texture measure.

In this paper, each texture measure/feature have been calculated in all 4 orientation angles (0°, 45°, 90°, 135°) and feature

measures of the matrices of all the four orientations arethen averaged, to get a directionally invariant matrix.

Displacement value d was chosen as 1.Window size was again 5*5 and quantization level as 64, which was sufficient and

efficient as established by [20], who said that quantization level above 24 performs well.

PARAM IMAGE

5

6

7

8

9

10

11

12

Section 5. Artificial Neural Network (ANN) Classifiers

In this section, the three ANNs used, viz., the MLP(Multi-Layer Perceptron), RBF-NN (Radial Basis Function - Neural

Network) and CC-ELM (Circular Complex valued Extreme Learning Machine) are discussed and finally a network model is

proposed for the purpose of classification using the Multispectral and SAR (Synthetic Aperture Radar) data.

Section 5.1 The Multi-Layer Perceptron Neural Network Classifier (MLP)

Multilayer Perceptron (MLP) model is a feed forward network. The MLP model chosen for the data set is a three layered

network as shown in figure 1.

Figure 1. The input layer has the number of nodes equal to the dimension of the input vector of each sample in the data set, viz., 8 (7 Multispectral + 1 Radar bands) and 10 (7 Multispectral + 3 Radar bands) resp. for each of the phase of running the classifiers on individual textural parameter filtered SAR images and of that when its ran on 3 textural parameters together.The hidden layer neurons possess a non-linear activation function in the form of a sigmoid function in this case. The output of sigmoid function of the hth hidden neuron is given by, yh = 1 / (1+exp(-ah) , where , (1) ah = ∑(xi*whi) + bh (2)

Here , xi is the input from the ith node , whi is the weight connecting hth hidden neuron to the ith input node and bh is the bias connected to hth hidden neuron. An MLP with a single hidden-layer is used as shown in figure 1 is used in this problem. The increase in the hidden neurons results in the increase in network classification because, more number of neurons results in fitting better boundaries between the classes in data distribution, as each neuron has a capability to draw an imaginary hyper-plane in the sample space and thus creating a partition. There exists a threshold value after which the increase in the number of hidden neurons amounts to decrease in the classification accuracy as it starts to over-fit and becomes less generalized. Hence, the number of hidden neurons is optimally set depending on the overall performance of the network. The output layer neurons have a purely-linear activation function , i.e. , the activation of an output neuron is it’s output . For output vector is an m-dimensional vector , where , m is the number of classes present in the final classified image . The output vector is considered to be of the form ,

Yt = [ Y1t … Ykt … Ymt ]T (3)

Ykt = 1 if Ct=k , else 0 (4)

Where, ct is the class to which the th sample/observation belongs to. The error is obtained by calculating the sum of squared difference between the Output and Target value for each sample. Thus the error is given by,

E = ∑i=1No(Yi - Ti)2 (5)

Where No is the number of output nodes ; Yi is the output of the ith node and Ti is the target value. The training on the neural network is carried out by presenting training set to the network and the weights are adjusted through iterations. The weights are initialized randomly. In every iteration, the network output is calculated and the error is determined. This error is used to adjust the weights of the network by using Scaled Conjugate Gradient method. The training is continued till error is minimized to a desired value. Training aims at minimizing the output error and optimizing the network weights. After training, the network is checked for the measure of generalization and performance using the testing set. Section 5.2 Radial Basis Function Neural Network Classifier (RBF-NN) A radial basis neural network (RBF-NN) is a commonly used alternative to ML-NN. RBF-NN is also a feed forward network. It is structurally similar to MLP-NN as shown in figure 2, but the activation function in the hidden layer nodes is called radial basis activation function. The output of the activation function depends on the location of the centre of the function and the spread of the function. The output of a radial basis function can be defined as ,

ɸ(x) = exp ( -|| x – c ||2 / 2*σ2 ) (6)

where, c is the centre of the RBF unit , x is the input and σ is the spread of the RBF unit .

Figure 2. Figure 3. The inputs are first normalized using suitable normalization. This activation function in the hidden layer produces a non-zero response when the input falls within kernel function. Each hidden unit has its own receptive fields in input space. The weights connecting the inputs to the hidden layer decide the spread of the activation function and the weights connecting the hidden layer to the outputs is used as a scalar multiplier to the hidden layer outputs. The network output is the sum of weighted hidden layer outputs. The training of the network involves the adjustment of the two sets of weights and updating of centers of the hidden nodes. The mean squared error is determined between the network output and the target output values. The centers and weights are initialized randomly. The weights and centers are optimized using Gradient Descent Back Propagation algorithm, by minimizing the instantaneous mean squared error. The error cost function is defined as shown in equation (5).The training algorithm aims at minimizing the error and the optimization of the weights and the center location. The training is carried out till the target performance is reached. The network is then tested for performance and generalization using the testing dataset. Section 5.3 Circular Complex Extreme Learning Machine (CC-ELM) R. Savitha [23] et al., presented a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’for handling real-valued classification problems. CC-ELM is a single hidden layer networkwith non-linear input and hidden layers and a linear output layer. A circular transformationwith a translational/rotational bias term that performs a one-to-one transformationof real-valued features to the complex plane is used as an activation function for the inputneurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like(‘sech’) activation function. The input parameters of CC-ELM are chosen randomly andthe output weights are computed analytically. The input and output configurations of CC-ELM are similar to that of MLP and RBF-NN. In CC-ELM the input layer activation function is ‘Circular Transformation’, which maps the real valued data to the complex domain, as in figure 4. The transformation is given by,

zt = sin ( axt + ibxt + α ) (7) where, xt is the input vector for the tth sample/observation,such that each of xti is normalized in [0,1]; 0 < a, b ≤ 1, and 0

Figure 4.

The bias term ensures randomness in the distribution of the feature vector and prevents overlapping in the complex plane. This also results in the efficient use of orthogonal decision boundaries. The above function is analytic and bounded in [0,1] and the input is mapped is mapped to [0,1] hence, as mentioned in [24] it is a valid activation function. The weighted response of the input layer is sent to the hidden layer. The hidden layer employs a Radial Basis activation function as in the RBF networks but in the complex domain. The hidden layer response of jth hidden neuron is,

zht = sech ( σjT ( z - cj ) ) (8)

Where, σj ϵ Cm is a scaling factor of the activation function , cj ϵ Cm is center of the activation function. As mentioned in the previous section, each neuron has 2 orthogonal surfaces as decision boundaries. This divides the feature space into 4 sections instead of 2, as in the case of real valued neurons. This contributes to higher classification efficiency as shown by Nitta [25]. The plot of magnitude and phase of the activation function is shown in figure 5 and figure 6, respectively. Each of these hidden neurons is connected to the neurons in the output layer. The output neurons calculate the weighted sum of the response of hidden layer neurons, which is effectively the output of the network. The targets are class coded as,

Yt = [ Y1t … Ykt … Ymt ]T (9)

Ykt = 1+i if Ct=k , else -1-i (10)

Figure 5. Figure 6. Given the matrix of output weights as Voxh and the response of hidden neurons as H, and the output of the network Y, the training procedure reduces to finding the least squares solution Voxh of the system:

Y = VH (11) In the case where number of hidden neurons is equal to the number of training samples, H is invertible. But in a practical scenario, number of hidden neurons is much less than the number of training samples; hence H may not be invertible.

Hence there may not exist a H-1 and V that satisfies the above equation. Therefore the solution with the minimum norm of least squares is,

Ѵ = YHǂ (12)

Where, Hǂ is the Moore-Penrose Pseudo inverse of the hidden layer output matrix [26] and Y is the complex-valued coded class label. Section 5.4 Proposed Model for the Network Architecture

Figure 7.

Section 6. Preparation of the Data Set Section 6.1 Down sampling the High Resolution SAR data In order to use both the Multispectral and SAR data in parallel to generate the data set , it is important that both the data can be linked in a way to benefit from the mutual information that they contain. The simplest way is to down sample the Higher resolution SAR image (18m) to the resolution of the Multispectral image (30m) and then co-register both the images. The method employed to down-sample the SAR image was based a simple weighted average.

Figure 8

Section 6.2 Co-registration of MS and SAR image The co-registration of the two data in the form of MS and SAR images is a quintessential component to proceed further with the classification. The co-registration was done manually by examination using the ERDAS IMAGINE 9 software and then corresponding lateral shifts were applied on the images.

Figure 9 : Before Co-registration Figure 10 : After Co-registration

Section 6.3 Extracting the Training and Testing data set from the MS and SAR images

The Training and Testing data set were prepared by extracting pixels from the co-registered image using pre-constructed AOIs (Area of Intrest) , carefully designed for each of the 4 classed (LAND,URBAN,VEG,WATER). The design process of the AOIs were constantly verified with Google Earth data.

Figure 11 : AOI designed to extract WATER pixels The properties of the data set prepared is as follows,

Section 7. Performance evaluation of the various parameters of texture filtering Clausi ,et al. [22], mentioned that three texture measures that provide different information taken in combination can give better classification than individual texture features being used. The goal of this section was to find the parameters of texture filtering for the SAR image, which suit best for improving the classification accuracies. The 8 parameters chosen were ASM, CON, COR, DIS, ENT, HOM, MEAN, VAR. The classifiers were run on the training and testing data set for these 8 parameters individually, i.e., 8 inputs ( 7 MS bands + 1 Texture Filtered

SAR image corresp. to a single parameter ) were fed to the classifiers. The results are presented in Table 1. And Table 2. corresp. to results obtained from R-RBF and CC-ELM respectively.

Table 1.

Table 2. It was noted from the above results that ENT and COR perform the best, and the third parameter to be chosen is still in doubts. Hence four combinations of 3 parameters taken together were formed, so that the classifiers could justify as to which one gives the best results. The four combinations chosen were, The classifiers were ran on the above four combinations of data , i.e, 10 inputs ( 7 MS + 3 SAR texture filtered, where the 3 SAR inputs corresp. to one parameter of texture filtering for each of the combinations) were fed to the ANNs to find which one gives the best results of them all. Table 3. And Table 4. provide the results obtained from R-RBF and CC-ELM resp. on the four combinations.

Table 3.

Legend ------------------------------- 5 - ASM 6 - CON 7 - COR 8 - DIS 9 - ENT 10 - HOM 11 - MEAN 12 - VAR

• 13 - ENT,COR,MEAN

• 14 - ENT,COR,CON

• 15 - ENT,COR,DIS

• 16 - ENT,COR,HOM

Table 4. It is observed from the above results that the combination 16 provides the best results in both the cases. Hence the combination of ENT, COR and HOM is chosen to prepare the data set for the cause of classifying the entire data. Section 8. Qualitative Analysis of the Classified Images obtained from the ANN classifiers The following results were observed on the training and the testing data sets.

Section 8.1 Running the classifiers on Patches of the entire data The classifiers were run on patches of images from the bigger data and the results are presented below and are followed by a qualitative analysis of the results obtained. Original MS MLP R-RBF CC-ELM

For the above results it can be observed that the MLP results are satisfactory to a fair extent. The CC-ELM results on patches 4 and 5 are not convincing as it misclassifies many VEG pixels as LAND pixels and detects a fewer URBAN pixels than what it had to. R-RBF on patch 4 produces similar results to what CC-ELM produced, viz., misclassified VEG pixels as LAND pixels. Section 8.2 Running the classifiers on the Full data Below are the results obtained after classification of the entire image using the data set prepared from the results of Section 7, using the ANN classifiers. MLP R-RBF CC-ELM

The R-RBF classified image shows that there has been a lot of misclassified pixels, mainly other categories (especially URBAN) misclassified as WATER pixels. The CC-ELM results show almost no valid classification on the bigger image, though it showed acceptable results on the smaller patches. Section 9. Conclusion It is difficult to define the most suitable texture measure to incorporate into the per-pixel classification of SAR image, but the findings presented in this paper are important for considering which texture measures can be used for accurate classification. First it was inferred that texture measures that are best for land-use/cover classification in SAR land images are Inverse Difference Moment, Entropy and Correlation. The results obtained via MLP classifier are satisfactory, while there were a lot of short comings in the results obtained from R-RBF and CC-ELM. The possible improvements to re-work on can be:

Automated co-registration of MS and SAR data, instead of the manual co-registration as done in the above work.

Generate a better training and testing data set, if the already generated data set isn’t sufficient.

Investigate as to why the results obtained were didn’t include much variation even with so many combinations of data.

Trying to achieve more generalized performance for the ANN classifiers instead of over-fitting the Training data set.

Investigate as to why R-RBF and CC-ELM didn’t provide better results than MLP, on the same data set, as is proven theoretically.

Investigate why CC-ELM isn’t providing good results on the entire image.

Acknowledgements: We are grateful for the contribution by Vivekananda Shankaracharya , S N Omkar , J. Senthilnath for their immaculate work on crop classification using supervised learning techniques and their guidance regarding the proceedings of the project.

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