Reformas educativas (LOEI Reglamento a la LOEI Plan decenal)

Based on the idea that different feature types on the earth's surface have different spectral reflectance and remittance properties, their recognition is carried out through the classification process. There are various classification approaches that have been

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developed and widely used to produce land cover maps (Aplin, 2006). Satellite image classification plays a major role in extract and interpretation of valuable information from massive satellite images. Satellite image classification methods can be broadly classified into three categories (see figure 2.5) and all three methods have their own advantages and disadvantages

(1) Automatic: These classification methods use algorithms that applied systematically the entire satellite image to group pixels into meaningful categories. Automated satellite image classification methods can be further classified into two categories - supervised and unsupervised classification methods. The supervised classification requires that the analyst inputs training sets. These training samples are the most important factor in supervised classification. Accuracy of the methods highly depends on the samples taken for training. Training samples are two types, one used for classification and another for supervising classification accuracy. Major supervised classification method uses the following statistical techniques:

i Artificial Neural Network (ANN) ii Binary Decision Tree (BDT) iii Image Segmentation

Unsupervised classification technique uses clustering mechanisms to group satellite image pixels into unlabeled classes/clusters. Later analyst assigns meaningful labels to the clusters and produces well classified satellite image. Most common unsupervised satellite image classification is

i ISODATA (Al-Ahmadi, et al 2009) ii Support Vector Machine (SVM)

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iii K-Means (Ahmed, et al, 2009)

(2) Manual: Manual satellite image classification methods are robust, effective and efficient methods. But manual methods consume more time. In manual methods the analyst must be familiar with the area covered by the satellite image. Efficiency and accuracy of the classification, depends on analyst knowledge and familiarity towards the field of study Sunitha, et al (2015).

(3) Hybrid: This method is a combination of the automated and the manual methods.

Automated method is used for the initial classification before manual is employed to refine and correct the errors.

Figure 2.5: Satellite image classifications methods hierarchy, Source: Sunitha, (2015)

This research has used the Maximum Likelihood Classification (MLC) for the pixel-based classification method and the Rule pixel-based classification for the object-pixel-based method.

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2.9.1.1 Maximum Likelihood Classification (MLC)

The MLC is one of the most popular supervised methods in which a pixel with the maximum likelihood is classified into the corresponding class. It is a parametric statistical method where the analyst supervises the classification by identifying representative areas, called training zones. The computer algorithm uses these zones to classify the pixels into spectral classes that are most alike. It is assumed that the distribution training data is Gaussian (normally distributed). During classification, all unclassified pixels are assigned class membership based on the relative likelihood of the pixel occurring within each class probability density function (Lillesand et al, 2004).

Maximum likelihood classifier may have difficulty distinguishing the pixels that come from different land cover classes but have very similar spectral properties. As a result, may lead to ‗salt and pepper‘ effects in classification maps especially when many mixed pixels are involved.

2.9.1.2 K-Nearest Neighbour (KNN)

K-NN is a parametric classification method and when you say a technique is non-parametric, it means that it does not make any assumptions on the underlying data distribution. It is one of simplest algorithms available for supervised learning. Despite its simplicity, k-NN can outperform more powerful classifiers and is used in a variety of applications (Zakka, 2016) It is called nearest neighbor because classification depends only on the nearest neighbor. The classification is based on a majority vote of the k-nearest neighbours, based on Euclidean distance in feature space, whereby 'k' specifies the number of neighbours to be used. In k-NN classification, the output is a class

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membership. An object is classified by a majority vote of neighbours, with the object being assigned to the class most common among its 'k' nearest neighbours (k is a positive integer, typically small). If k=1 then the object is simply assigned to the class of that single nearest neighbor.

Some advantages of k-NN are that it is simple to understand and easy to implement.

Furthermore, k-NN works just as easily with multiclass data sets whereas other algorithms are hardcoded for the binary setting. Finally, the non-parametric nature of k-NN gives it an edge in certain settings where the data may be highly ―unusual‖. On the other hand, k-NN has computationally expensive testing phase and can suffer from skewed class distributions if a particular class is frequent in the training set. Finally, the accuracy of k-NN can be severely degraded with high-dimension data because there is little difference between the nearest and farthest neighbor.

2.9.1.3 Supervised Object Based Classification

There are two main approaches in object based image classification – the supervised and the rule based methods. The supervised is very similar to pixel based supervised classification, based on selection of training samples that are used to train the classification algorithm. However for supervised object based method, instead of single pixels or random group of pixels, compact image objects with calculated features (statistics), which are result of image segmentation process, are selected. Supervised classification algorithms include Nearest Neighbor (NN) classification (e.g., Jensen, 2005), Standard Nearest Neighbor, Fuzzy membership functions (Benz et al., 2004) and others.

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2.9.1.4 Rule Based Classification Method

The rule based classification method has been employed in the object based analysis of this research. This method does not use any samples for the classification, but is based purely on the expert knowledge of the user. The user assigns objects to classes based on expertise/prior knowledge. A set of conditions or rules commonly referred to as rule set is developed by the user for each target class. The rule set development makes use of image object features, such as spectral mean value, size, shape, texture, or different contextual image features are used in the rule set development to assign image objects that fulfill the criteria to the respective classes. One advantage of this method is that the users as a result, has full control of the classification process and able to confidently determine where image objects belong. Another advantage is that the rule set is transferable to another image, so it can be re-used again in another scene or project. The ability to reuse the rule set later completely as it is or with minor manual modifications makes it a very valuable approach.

Rules are created based on human knowledge and reasoning about specific land-cover types (ENVI-Zoom 2010). For example, dark building has a low NDVI, roads are elongated, buildings are rectangle in shape, water has a low mean value in NIR band, and vegetation has a high NDVI and trees are highly textured compared to grass. To extract specific features, multiple rules can be defined to separate unwanted features from targeted features, and assigning wanted objects to desired feature class (Hamedianfar 2014). In this research rule sets were defined for classification based on available spectral, spatial and textural characteristics.

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