An Intelligent Assessment of Land Cover Classification by using Spectral and Statistical Texture Data Analysis

Salman Qadri*[a-b], Muzammil-ul-Rehman[a], Mutiullah[a], Muhammad Amjad Iqbal[b], Muhammad Nazir[b] [a] Faculty of Information Technology, The University of Central Punjab Lahore, Punjab 54000, Pakistan [b] Department of Computer Science & IT, Faculty of Management Sciences, The Islamia University of Bahawalpur, Punjab 63100, Pakistan.*Author for correspondence; e-mail address: salman.qadri@iub.edu.pk

ABSTRACTThe main objective of this research was to find out the importance of machine vision approach for the classification of five types of land cover (LC), fertile, green pasture, desert-range land, bare and Sutlej-river land. A novel spectra-statistical frame work was design to classify the subjective land cover types accurately. The above mentioned five types of land cover have strong correlation among each other. On the basis of human perception, among these three selected land cover like desert rangeland, Sutlej river land and bare land have almost similar physical features and remaining two fertile cultivate land (cropland) and green pasture (grass) are similar. Remote sensing data of these five types land was acquired by using handheld crop scan device MSR5 in the form of five spectral bands (blue, green, red, infrared and microwave) while statistical texture data was arranged with a digital camera by the transformation of acquired images into 229 statistical texture features for each image. out of which the most discriminant 30 features were obtained by integrating the three statistical features selection techniques, Fisher co-efficient (F), Probability Of Error plus Average Correlation Co-efficient (POE+ACC), and Mutual Information Co-efficient (MI), while no such feature selection procedure was required for spectral data because in this data each scene was completely define on the basis of above mentioned only five spectral bands. Capability of selected statistical texture data clustering was verified by Non Linear Discriminant Analysis (NDA) and Linear Discriminant Analysis (LDA) approach was applied for spectral features. For classification, these statistical and spectral features were deployed to artificial neural network (ANN). By implementing cross validation method (80-20) we received an accuracy of 91.3221% for statistical texture data and 96.40% for spectral data respectively.

Keywords: Textural features, Remote Sensing, Artificial Neural Network, Land Cover, Mazda Software Version 4.6.

1. INTRODUCTIONImage processing and remote sensing can play a vital role for betterment of the agriculture field [1]. By using this technology, we can classify vast land cover area into different categories [2] .This would be helpful not only for the socio-economic sector but also fulfill the needs of future. In twenty first century, world is facing the challenge of hunger, food and poverty [3]. This issue can be resolved by increase in crop production and better utilization of cultivated land. Land cover information is necessary for different policy making, planning and management purposes like, land record of forest, desert, farmland, and wetland as well as other biophysical resources are required for land cover information. Researchers are trying to get the benefits of technology by involving it in agriculture field [4]. It is being tried to enhance the cultivated land area and monitored the land through intensive manual survey [5]. For the success of such surveys a heavy economical and labor investment is required. In developing countries like Pakistan, it seems to be very difficult to spend huge amount on such projects. Whether directly or indirectly almost 50% population of these countries is associated with agriculture profession [6]. All above discussed issues highlight the importance of the proper classification, management, better utilization, crop growth and production of the land. According to geographical distribution of land, it is categorized into different types like barren, fertile, rocky and sandy etc. In Pakistan, the conventional field based survey system could not be properly managed due to both economical and technical limitations. For this reason, remote sensing technology has not been utilized for natural resource management up till now, as was proposed by the relevant professionals [7]. A two layer Conditional Random Field (CRF) model for land cover and land use classification was proposed [8]. Similarly a multilayer conditional random field (MCRF) land classification model was suggested. It was used for multi temporal with multi scale remote sensing data [9]. A gray level co-occurrence metric with different window size images were used to find the four land type of aerial data. Different statistical features like dissimilarity, homogeneity, angular second moment; entropy etc were calculated to classify the data [10]. A supervised pixel-based classification algorithm was used by implementing Markov Random Field (MRF) technique to distinguish the agriculture land cover area (cropland and grass land) [11]. It gave the satisfactory results for updating in GIS database for the cropland and grassland region.In this research, it was tried to involve the technologies like (image processing and remote sensing) in land cover classification instead of manual surveys. This research was conducted at district Bahawalpur of Punjab (Pakistan) province, located at 292344Nand 71411E. This research focused at the land cover assessment, management and classification through remote sensed data. This data was acquired by using a device named Multispectral Radiometer Crop Scan (MSR5). It is a handheld device which provides data equivalent to Satellite Landsat5 TM (Thematic Mapper). MSR5 provides an alternative way of acquiring data for remote sensing where satellite or radar datasets are not easily available. Its output data comprises five spectral bands visible (blue, green, red), infrared and microwave ranges from 450nm to 1750nm, whereas, photographic data was acquired by a digital camera. This research will be based on the analysis of 5 types of land cover datasets, bare land, fertile cultivated land, green pasture, desert rangeland and Satluj river land.

2. MATERIAL AND METHODSThe aim of this research work was to develop a simple, concise and robust method to classify the five types of land cover (LC) in an absolute natural environment by using spectral and statistical texture features. To avoid complex laboratory setup for the extraction of morphological and color features, we used statistical textural features for photographic data and spectral features for remote sensing data in this work. Excellent results with an average accuracy of 91.332% for statistical texture features data and 96.40% for spectral data were achieved. We have proposed a novel spectra-statistical design frame work for subjective land cover (LC) classification. To accomplish this study the following procedural steps of image preprocessing, feature extraction, feature selection, feature reduction and classification are adopted, which are discussed in the following sections.

2.1 PROPOSED METHODOLOGYFigure 1, describes the proposed spectra-statistical design frame work of this study.

Figure 1. Proposed spectra-statistical design framework

2.2 STATISTICAL DATA AND IMAGE ACQUISITION Above mentioned five different types of land cover (LC) plots having 43560 square feet area (1 acre) for each type. Digital photographs of bare land, desert rangeland, fertile cultivated land, green pasture and Sutlej river land are acquired by digital camera of Company; Nikon, model Coolplex having a resolution of 10.1 megapixels. The 15 colored images of each type of land cover with the dimensions of 42883216 pixels and 24 bits depth having jpg format are obtained. To increase the dataset, 4 non overlapping regions of interests (ROIs) of size (512x512) on each image were developed, in this way total 300(754) sub images data was arranged for the analysis. The photographic data was acquired at the altitude 5 feet from the ground surface. To avoid the shadow effect the imaging was performed at noon time (12.00 pm to 2.00 pm) under clear sky. At the time of data acquisition the light intensity was recorded by digital Luxmeter MS 6610, MATECH.

Table 1. Time and Light Intensity InformationSr. NoLand TypeTimeLight Intensity

1.Fertile1.00 pm34300 lux

2.Bare2.00 pm34000 lux

3.Desert1.30 pm34500 lux

4.Green Pasture1.30 pm35000 lux

5.Sutlej River1.00 pm34300 lux

Figure 2. Five Land Cover Images and Luxmeter

2.3 REMOTE SENSING DATA ACQUISITIONRemote sensing is a science, which detect or identify an object or body without physical interaction. For this purpose, a device, which is equipped with some sensors identify the object with its spectral parameters. For remote sensing different satellites, radar and space stations are working in space. These satellites have their sensitive cameras and sensors, which are used to detect the radiations of each object and record the numerical values of spectral data. Remote sensing can be defined as the collection of data in the form of radiations about an object taken from a particular distance [12]. Remote sensing is now playing a significant role in a wide range of environmental disciplines such as geography, geology, zoology, agriculture, forestry, botany, meteorology, oceanography and civil engineering [13].

2.4 MULTISPECTRAL RADIOMETER (MSR5) Multispectral Radiometer (MSR5) made-up of CROPSCAN Inc. (USA) for data collection. MSR5 has the characteristic to provide data similar to satellite LANDSAT5 TM. It has five different section of spectrum, including visible (Blue, Green, Red) near infrared and shortwave infrared. MSR5 spectrum consists of blue (450 to 520 nm), green (520 to 600 nm), red (630 to 690 nm), near infrared (760 to 900) and shortwave infrared (1550 to 1750 nm).MSR5 CROPSCAN has been already used for the assessment and measurement of crops weed effect [14]and vegetation cover estimation and diseases estimation [15-16]. For remote sensing data, we acquired 50 MSR scans of each plot at 5 feet height of land cover surface. Each MSR scan contain five wave bands, three visible (Blue, Green, Red) and two invisible infrared and microwave. Five different types of land cover[17] contain total 250 spectral data instances.

2.5 PREPROCESSINGEach image has a vast irrelevant area, so prior to further processing relevant portion of the image was extracted. The extracted relevant portions of the images were converted to gray scale images (8 bit) and were stored in bmp format because the software MaZda which was used to calculate texture features only works for this format [18]. By using image converter software we enhanced the contrast of gray scale images.

2.6 FEATURE EXTRACTION Transformation of an image in to its statistical attributes is called feature extraction, which were used for the classification of an image. There were many techniques for feature extraction e.g. texture, Gabor, wavelet transform, boundary feature etc.

2.7 TEXTURE FEATURES Statistical texture features are categorized in to first order which relates to the intensity of the individual pixels, Second order relates to the occurrence of neighboring pixels. First order statistical parameters are directly based on histogram features of an image while second order parameters derived from Gray Level Co-occurrence Matrix (GLCM). Here in this work total 229 statistical texture features were calculated for each region of interest (ROI) by using Mazda software version 4.6. The calculated parameters are grouped as first order 9 statistical parameters and 11 second order (Haralick) statistical parameters derived from (GLCM) in all four directions (0, 45, 90 and 135) up to 5 pixel distance 220 (1145) Haralick et.al [19]. It means that each ROI had defined by 229 textural features and statistically the data was presented in 68700 (300229) dimensional features vector space. It is worth to be mentioned here that all of the 229 calculated features were not equally important regarding for land cover classification. Furthermore, statistically a huge data was required to have a reliable discrimination and classification results on the basis of so large number of features, generally, which was not available. So, it was necessary that feature vector space dimensionality should be reduced by selecting the most discriminate features, which had the ability to discriminate and classify the different types of these land cover classes.

2.8 FEATURES SELECTIONSelection of the most suitable features for the classification was a challenging task. We had used three supervised feature selection methods Fisher Co-efficient (F), Probability Of Error plus Average Correlation Co-efficient (POE+ACC) and Mutual Information Co-efficient (MI). These methods were merged together (F+PA+MI) to get the most discriminant features. Fisher Co-efficient (F) [20] mathematically is described as:(1)

Where Between-class variance, within-class variance, probability of feature ,, variance and mean value of feature in the given class.

Probability of Error plus Average Correlation Co-efficient (POE+ACC) [21] is defined as:

(2)

(3)

(4)

Mutual Information Co-efficient (MI) [22] can be explained by the given mathematical relation.(5)

These approaches were used for the selection of the most discriminant set of features. This software selects the 10 most significant features and presents these features in descending order according to their significance. In this way total 30 (10 features by each mentioned approach) were selected. As the combined set of features gave better classification results [23], hence all the above mentioned features were merged together, in this way a set of 30 features were obtained for further procedures.

Table 2. Feature Table (F+PA+MI) for ROI (512x512)FPAMI

1S (0,3) Correlation2S (0,4) Correlation3S (0,3) Contrast4S (0,4) Contrast5S (0,5) Correlation6S(0,5) Contrast7S (2,2) Correlation8S(0,3) Sum Variance9S(0,1) Inv Diff Mom10S(0,4) Sum VariancePerc.01%S(1,1) Sum VarianceS(0,1) Ang. Sec Mom SkewnessS(0,2) Sum VarianceS(5,5) EntropyS(5,-5) Inv. Diff. MomS(1,0) Sum. AverageS(1,0) CorrelationS(3,3) EntropyS(0,5) Inv. Diff. MomS(5,-5) Inv.Diff. MomS(0,4) Inv. Diff. MomS(4,-4) Inv.Diff. MomS(0,3) Inv. Diff. MomS(3,-3) Inv.Diff. MomS(0,2) Inv. Diff. MomS(2,2) Inv .Diff. MomS(2,-2) Inv.Diff. MomS(0,1) Inv. Diff. Mom

2.9 FEATURES REDUCTIONPrior to classification the features data was standardized to reduce the effect of unwanted variation within the data due to outliers and other artifacts by applying the following mathematical relation:(6)

Where: is the standardized value of feature and i = 1, 2, 3 . Original feature value Mean feature value Standard deviation

The above mentioned approaches of feature selection (F+PA+MI), only select the most significant parameters, but did not directly express the degree of discrimination power. To find the classification and data clustering, the selected 30 features data was deployed to non-linear discrimination analysis (NDA) available in B11 software [24], In this technique there were 3 layers (input, first hidden, second hidden and output layer) of processing elements (neurons) are present. NDA can be described by logistic function. Its value equal to 0.5 for, and it changes smoothly from 0 to 1 for varying from large negative to large positive values. (7)

If X was the feature vector and it was the input to artificial Neural Network (ANN). The input terminals were equal to Nx. Vector Y was the output of ANN, whose dimension Ny was equal to the number of types in the dataset. Thus the ANN had Ny output terminals(8)

Now here n = 1, 2, 3 (9)

Here k = 1, 2, 3 (10)

While j = 1, 2, 3

Supervised learning methods were based on input patterns and correct classes they belong to, {xi, di} where i = 1, 2, 3 . For this purpose, the following error function

(11)

While for MSR5 datasets linear discrimination analysis (LDA) gave the best results for data clustering and analysis. Let denote the pattern in class i, where i = 1, 2, 3 , and k = 1, 2, 3 . Define the within-class scatter matrix as:

(12)

Where was the mean vector of class . Similarly, define the between-class scatter matrix as (13)

Where was the mean vector of the pooled data. The total scatter matrix was the objective of linear discriminant analysis (LDA) was to get a linear transform matrix.

(14)

2.10 CLASSIFICATIONFor this work, we had applied supervised classification Artificial Neural Network (ANN). ANN classifier was implemented because of two reasons; firstly we had supervised data (due to five types of land) secondly according to [24], ANN is a robust approach for noisy and incomplete data (such factors were always present in dataset which was acquired in natural environment). The implemented classifier based on feed forward approach with a single hidden layer of sigmoidal neurons. If x was the number of deployed input features vectors to ANN classifier then input terminals were equal to Nx. The output feature vector was y, whose dimensions Ny was determined by the number of classes to be classified. Thus the ANN had Ny output terminals.

Figure 3. Model of implemented ANN classifier

(15)

Where k = 1, 2, 3 and the outputs of the hidden layers are given as:(16)

We see here that j = 1, 2, 3 .

For train and testing purpose, the weight coefficients are adjusted and observed how much actual output value Y is close is to the desired output d. Supervised training techniques are, based on input patterns and correct categories they belongs to {xi, di}, where i = 1, 2, 3 then following is the error function which is reduced by changing of weights v and w.(17)

The architecture of proposed classifier is given for both types of dataset in the Table 3:

Table 3. Architecture of implemented Classifier for Statistical DatasetInput Layers=51st Hidden Layer=52nd Hidden Layer=2

Learning Rate Eta=0.25Back Propagation Iteration=200000Optimized Iteration Limit=70

Output Layers=5

Statistical data classification architecture is given Table 4.

Table 4. Architecture of implemented Classifier for Spectral DatasetInput Layers=51st Hidden Layer=52ndHidden Layer=2

Learning Rate Eta=0.20Back Propagation Iteration=200000Optimized Iteration Limit=70

Output Layers=5

3. RESULTS AND DISCUSSIONSThe proposed methodology had been implemented by using Mazda software versions 4.6 on Intel(R) Core i3 processor 2.4 GHz with 64-bit operating system.

3.1 PHOTOGRAPHIC DATAFor photographic dataset, first attempt for data clustering and land cover classification was verified on the basis of features selected by individual F, POE+ACC and MI approaches on the basis of ROIs ,, and. Now the selected features were deployed for raw data analysis (RDA), principle component analysis (PCA), linear discriminant analysis (LDA) and non-linear discriminant analysis (NDA) projection spaces to verify the capability of data clustering. Here, we received better data clustering on the basis of NDA approach as compare to the other three approaches. It was observed that the discussed above first three ROIs did not give satisfactory results. These As we have received less than 70% accuracy on the basis of these three ROIs which is not acceptable whereas for ROI we received 80%, 84% and 88.324% classification accuracy on the bases of feature selected by F, POE plus ACC and MI respectively in projection space of NDA. Because it has been reported by number of researcher usually the classification is proportional to the number of features deployed [25], we also implemented the same strategy to have better results. For this purposes we merged the features selected by already discussed three approaches (F+PA+MI). In this way, a set of 30 features (10 features of each selection method) was received by combining these three approaches on ROI. On deploying these 30 features to RDA, PCA, LDA and NDA, These datasets are deployed on the above feature reduction by using the k-fold (80-20) cross validation method. It was observed that nonlinear discriminant Analysis (NDA) has given better analysis of 100% as compared to others three features reduction analysis approaches. The results are summarized in Table 5.

Table5. Statistical Data Analysis Table by using different features Reduction approaches.Statistical Data Analysis K-(80-20)RDAPCALDANDA

1-Fold92.5%92.50%97.50%100%

2-Fold88.75%87.92%96.25%100%

3-Fold90%89.17%98.75%100%

4-Fold88.75%87.50%96.67%100%

5-Fold90.42%90.42%99.17%100%

Average Accuracy90.08%89.502%97.668%100%

The Statistical data comparison analysis of RDA, PCA, LDA and NDA is presented in Figure 5. From this Figure, it is clear that the result NDA leads for best classification result of 100% accuracy as compared to remaining three approaches RDA, PCA and LDA. Figure 5 represents the Photographic data clustering for five input classes in NDA projection space

Figure 5. Digital Photographic Data Analysis Result.

Non Linear discriminant Analysis (NDA) graph shows the properly clustered data in to its five appropriate classes. Data Cluster graph is shown below Figure 6.

Figure 6. Statistical data clustering in NDA.

By the implementation of ANN: n class training and testing, available in B11 integrated with software Mazda was performed to verify the validity of classifier. For this purpose a cross validation K-fold (80-20) method was used. For training purpose 48 data instances of each size (512512) from land cover type was used. For each iteration, total 240 data instances out of 300 were used for train the dataset. Testing is performed on 60 data instances (12 data instances from each land cover type). We received an average accuracy of 100% when the classifier was trained under the architecture setting already discussed in Table 3 and an average classification accuracy of 91.334% was obtained when classifier was tested for photographic data. So, five types of land cover data were clustered properly by using nonlinear discriminant analysis (NDA). Statistical texture train, test, properly classified along with misclassified data is represented in Table 6.

Table 6. Classification Table for Image dataset (Statistical data) by using Artificial Neural Network (ANN)Statistical Data Iteration (80-20)Training DataTrain Accuracy %Test DataMisclassified DataClassification %

1-Fold240100%605/6091.67%

2-Fold240100%606/6090%

3-Fold240100%606/5090%

4-Fold240100%603/5095%

5-Fold240100%606/5090%

Average Training classification: 100% Average Testing Classification: 91.334%

The performance of classifier in testing phase for different classes is summarized in confusion Table 7. Total 300 data instances of photographic data (60 data instance of each land) are shown in appropriate five different classes.

Table 7. Confusion Table of Statistical Data Classification by using ANN: n class MethodTypeFertile LandGreen PastureDesert RangelandBare LandSutlej River LandTotal

Fertile Land51132360

Green Pasture05910060

Desert Rangeland34483260

Bare Land11157060

Sutlej River Land03115560

Here confusion Table for photographic data is presented by using ANN: n class Method of five different land cover types by graphical way in Figure 7.

Figure 7. Confusion Table of Statistical Data Classification Graph.

3.2 SPECTRAL DATAAs we have already mentioned that a scene was completely explored on the basis of five spectral bands (Blue. Green, Red), infrared and microwave acquired by MSR5.The whole data (250 scans) acquired by MSR5 were deployed to RDA, PCA, LDA and NDA to verify the validity of data clustering for the classification. We received RDA 98.7%, PCA 98.4%, LDA 99.5% and NDA 99.4% data clustering accuracy. It is clear that we received the best clustering accuracy by LDA approach as shown in Figure 8. For training and testing ANN classifier, the same K-fold (80-20) cross validation method was also used for Spectral data analysis. A data set of 200 scans of five spectral parameters (Blue. Green, Red), infrared and microwave was deployed to ANN: n class training purpose with architecture settings as mentioned in Table4. The output training results for spectral data are summarized in Table8 and are represented graphically in Figure 6. Under the same architecture setting of Table3, ANN classifier was tested by deploying 50 disjoints data instances (10 data instances of each land cover type) of the selected five spectral features. Artificial Neural Network (ANN) classifier revealed very promising results during this testing purpose. The results of data clustering, training and testing are presented in Table 8 with detail.

Table 8. Spectral Data clustering by different features Reduction techniques Spectral Data Analysis (80-20)RDAPCALDANDA

1-Fold99%97.5%99.5%100%

2-Fold99%99%100%99%

3-Fold98.5%98.5%100%100%

4-Fold98.5%98.5%99%99%

5-Fold98.5%98.5%99%99%

Average Accuracy98.7%98.4%99.5%99.4%

Spectral data comparison analysis of RDA, PCA, LDA and NDA is shown in Table5; this shows that LDA outperforms and gives the 99.5% clustering accuracy. For Feature reduction techniques, data analysis graph of MSR5 is shown in Figure 8.

Figure 8. MSR5 Dataset analysis Results.

Linear discriminant Analysis (LDA) graph shows the properly clustered data in to its five appropriate classes as compared to employed other reduction techniques. Data Cluster graph is shown in Figure 9.

Figure 9. MSR5 Data Clustered Result for LDA

We received an average accuracy of 100% when the classifier was trained under the architecture setting already discussed in Table 4 and an average classification accuracy of 96.40% was obtained when classifier was tested for MSR5 data. So, five types of land cover data were clustered properly by using linear discriminant analysis (LDA). MSR5 train, test, properly classified along with misclassified data is represented in Table 9.

Table 9. MSR5 Dataset Classification (Testing Data (80-20 Fold)Spectral Data Iteration (80-20)Training DataTrain AccuracyTest DataMisclassified DataClassification%

1-Fold200100%506/5088%

2-Fold200100%502/5096%

3-Fold200100%500/50100%

4-Fold200100%501/5098%

5-Fold200100%500/50100%

Average Accuracy: 88+96+100+98+100 = 96.40%

Confusion Table of spectral data classification by using ANN: n class Method of five different types land is shown in given Table10.

Table 10. Confusion Table of Spectral Data Classification by using ANN: n class MethodTypeFertile Land Green PastureDesert RangelandBare LandSutlej River LandTotal

Fertile Land47111050

Green Pasture05000050

Desert Rangeland00482050

Bare Land00248050

Sutlej River Land00114850

Now confusion Table for MSR5 data is presented by using ANN: n class Method of five different types land is shown in given Figure 10.

Figure 10. Confusion Chart of Spectral Data Classification.

When we compare spectral and statistical classification accuracy it is observed that spectral accuracy result is better 96.40% as compared to statistical texture 91.334%. Comparison graph between MSR5 and photographic data is shown Figure 11.

Figure 11. Land classification Results between spectral and statistical data.

Reason behind this classification accuracy difference is that statistical analysis outperformed on fine texture [26]. In this research, the photographic data was taken at 5 feet height so the area under these photographs were not equally covered and distributed, beside this ROIs also play an important role for classification. As ROIs size increased then efficiency was also observed better. it was the fact that if photographs were taken on more height and area under the region was maximum covered then classification accuracy could be improved. Secondly it was observed that almost (5% to 6%) better classification results were obtained by the remote sensing MSR5 data as compared to photographic data (400nm to 700nm) because MSR5 data comprises visible (400nm to 700nm) and invisible infrared and microwave (790nm to 1750nm) wavelength. Data sampling with normalization and data standardization and classifier may also impact on results for better classification. By implementing these sophisticated quantitative parameters rather than conventional qualitative parameters we can accurately classify [27] the different types of land cover.

4. CONCLUSIONIn this work five types of land cover were classified on the basis of quantitative parameters rather than conventional qualitative parameters and an average accuracy of 96.40% for spectral data and 91.334% for statistical texture data was achieved. It was difficult task that, up to what extent these classes may be classified into their appropriate classes and it was also a verification of intra and inter classification pattern features of these five land cover types. Five Spectral and 30 statistical textural features were used for the analysis of land cover data which made our approach more reliable than other approaches in which morphological, Color and other shape features were used. Artificial neural network (ANN) was implemented very successfully for the classification of five different types of land cover, Fertile, Green pasture, Desert Rangeland, Bare and Sutlej river land. In future we may enhance this research for considering environmental factors like rain, usage of fertilizers; dry weather effects and land crop growth and assessment etc.

REFERENCES [1] WalterShea E., Blad B., Hays C., Mesarch M., Deering D. and Middleton E., Biophysical properties affecting vegetative canopy reflectance and absorbed photosynthetically active radiation at the FIFE site, Journal of Geophysical Research: Atmospheres., 1992; 97:18925-18934.[2] Rundquist B. C. Fine-scale spatial and temporal variation in the relationship between spectral reflectance and a prairie vegetation canopy, PhD Thesis, Kansas State University, U.S.A., 2000. [3] Foody G. M., Status of land cover classification accuracy assessment, Remote sensing of environment., 2002; 80(1): 185-201.[4] Parihar J. S. and Oza M. P., FASAL: An integrated approach for crop assessment and production forecasting, Proceedings of the SPIE, Agricultural and hydrology applications., 2006: 6411, 641101-641113.[5] Pakistan Demographic Survey, Province Census Report of Sindh, Federal Bureau of Statistics, Statistics Division, Government of Pakistan, Islamabad, 2000.[6] Kureshy K. U., In Geography of Pakistan, National Book services, Lahore, 1995.[7] Blaschke T., Lang S., Lorup E., Strobl J. and Zeil P., Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications, Environmental information for planning, politics and the public., In Cremers A. and Greve K., eds., Environmental information for planning, politics and the public, Metropolis-Verlag, Marburg, 2000; 2: 555-570.[8] Albert L., Rottensteiner F. and Heipke C., A two-layer Conditional Random Field model for simultaneous classification of land cover and land use, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., 2014;3: 17-24.[9] Hoberg T., Rottensteiner F. and Heipke C., Context models for CRF-based classification of multitemporal remote sensing data, ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences., 2012; 1(7):128-134.[10] Caridade C., Maral A.R. and Mendona T. The use of texture for image classification of black & white air photographs. International Journal of Remote Sensing., 2008; 29(2): 593-607.[11] Helmholz P., Rottensteiner F. and Heipke C., Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images, ISPRS Journal of Photogrammetry and Remote Sensing,, 2014; 97: 204-218.[12] Davies E.R., Machine vision: theory algorithms practicalities,1st. Edn., Elsevier, 2004.[13] Panigrahy S., Upadhyay G., Ray S. S. and Parihar J.S., Mapping of cropping system for the Indo-Gangetic plain using multi-date SPOT NDVI-VGT data, Journal of the Indian Society of Remote Sensing., 2010; 38(4): 627-632.[14] Tsirogiannis I., Katsoulas N., Savvas D., Karras G. and Kittas C., Relationships between Reflectance and Water Status in a Greenhouse Rocket (Eruca sativa Mill.) Cultivation, European journal of horticultural science., 2013; 78(6): 275-282.[15] Chang J., Clay S.A., Clay D.E. and Dalsted K., Detecting weed-free and weed-infested areas of a soybean field using near-infrared spectral data, Weed Science., 2002; 52(4): 642-648.[16] Vrindts E., Reyniers M., Darius P., Gilot M., Sadaoui Y., Frankinet M., Hanquet B. and Destain M.F., Analysis of soil and crop properties for precision agriculture for winter wheat, Biosystems engineering., 2003; 85(2): 141-152.[17] Szczypiski P.M., Strzelecki M., Materka A. and Klepaczko A., MaZdaA software package for image texture analysis, Computer methods and programs in biomedicine., 2009; 94(1): 66-76.[18] Gonzalez R. C., Woods R. E. and Eddins S. L., Digital image processing using MATLAB. Pearson Education India, 2004.[19] Haralick R. M., Shanmugam K. and Dinstein I.H., Textural features for image classification., IEEE Transactions on Systems, Man and Cybernetics., 1973; 6 : 610-621.[20] Schrmann J., Pattern classification: A unified view of statistical and neural approaches, Wiley Online Library, 1996.[21] Dash M. and Liu H., Feature selection for classification, Intelligent data analysis., 1997); 1(3): 131-156.[22] Tourassi G. D., Frederick E. D., Markey M. K. and Floyd Jr. C. E., Application of the mutual information criterion for feature selection in computer-aided diagnosis, Medical Physics., 2001; 28(12): 2394-2402.[23] Ojala T., Pietikinen M. and Harwood D., A comparative study of texture measures with classification based on featured distributions, Pattern recognition., 1996; 29(1): 51-59.[24] Materka A., Strzelecki M., Lerski R. and Schad L., Toward automatic feature selection of texture test objects for magnetic resonance imaging, Pattern Recognition Conference; Porto , 2000: 11-16.[25] Park S. C., Pu J. and Zheng B., Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers, Academic radiology., 2009; 16(3): 266-274.[26] Zapotoczny P., Discrimination of wheat grain varieties using image analysis and neural networks, Part I. Single kernel texture, Journal of Cereal Science., 2011; 54(1): 60-68.[27] Alajlan N., Bazi Y., Melgani F. and Yager R. R., Fusion of supervised and unsupervised learning for improved classification of hyperspectral images, Information Sciences., 2012; 217: 39-55.