Forestry Classification of Beijing Using Supervised

FORESTRY CLASSIFICATION OF BEIJING USING SUPERVISED & UNSUPERVISED CLASSIFICATIONSubject: Urban Remote SensingName: Muhammed Usman FaridStudent Number: 201429180003Dated: 2015/06/23INTRODUCTIONFor the completion of the course, I have selected the site Beijing and its forestry land use classification. As from the first glace of visual ground trooping we can say that, Beijing is a green city. Although for the representation of the forestry in the area, I have used the ENVI 4.7 Software. I have used the TM band Width 2 for to represent the site of forestry. I used the Supervised and Unsupervised classification of site by using ENVI 4.7 software.SITE AREABeijing, formerlyRomanizedasPeking. Beijing is the second largest Chinese city byurban populationafterShanghaiand is the nation'spolitical,cultural, and educational center. The Ariel map of the city is as under;

Figure 1: Map of Beijing

SUPERVISED CLASSIFICATION OF BEIJINGDevelopment of a classification scheme by selecting representative areas using reference sources such as higher resolution. Determine the number of cluster centers and initialize the cluster centers. Maximum likelihood supervised classification. MLC is performed according to the following steps. Display the three-band overlay composite image. Using box-cursor to choose representative training samples for each of the desired classes from the color composite image. Color-encode and show the classified image. Estimate the number of pixels and area for each class and show the statistics for each class.Supervised Classification Maximum Likelihood Map

Figure 2: MaxlikelihoodUNSUPERVISED CLASSIFICATION OF BEIJINGAnother broad of classification is unsupervised classification. It doesnt require human to have the foreknowledge of the classes, and mainly using some clustering algorithm to classify an image data. The computer uses techniques to determine which pixels are related and groups them into classes.The two most frequently used algorithms are the K-mean and the ISODATA clustering algorithm.In general, both of them assign first an arbitrary initial cluster vector.The ISODATA algorithm is similar to the k-means algorithm with the distinct difference that the ISODATA algorithm allows for different number of clusters while the k-means assumes that the number of clusters is known a priority.UNSUPERVISED CLASSIFICATION OF BEIJING USING ISO-DATA AND K-MEANS

Figure 2: ISO-DATA

Figure 3: K-MeansRESULTSThe selection of suitable band combination is essential for SPOT 3 image for visual interpretation. The images of Beijing Forest Reserve and its surrounding area appear much better after performed enhancement technique. In this study, the band combination of 4-3-2 (Red-Green-Blue) was selected as the best combination and later used for digital classification. Meanwhile Forest appear more expanded compared to the unsupervised technique. Maximum Likelihood Classifier (MLC) and the original mechanism of ISODATA classifier in the unsupervised technique. The vegetation mapping accuracy in both techniques referred to producers and users accuracies.CONCLUSIONThe study on evaluation of accuracy from the supervised and unsupervised techniques had produced a baseline for the Beijing Forestry. Accuracy assessment showed that the overall accuracies were less than 80% and 71% for unsupervised and supervised classifications, respectively. The advantage of classification is obvious. We can get the physical meaningful reflectance and their multivariate spreads. We can know the estimate the area coverage by forestry.

ADVANTAGES AND DISADVANTAGES OF SUPERVISED AND UNSUPERVISED CLASSIFICATIONSupervised classification Disadvantages The disadvantage of this approach is its computational cost, since performing wrapper FSS is slow.Unsupervised Classification Disadvantages As the analyst has little control over the groupings determined in unsupervised classification, assigning those groupings to reset classes can be more difficult and complicated. It should be noted, however, that in other situations where the final set of land cover classes is more open to adjustment this disadvantage may not be an issue in the classification. A fiction than the unsupervised method for similar reasons. The inherent disadvantages of the unsupervised method are advantages of the supervised method, and vice versa.ADVANTAGES AND DISADVANTAGES OF SUPERVISED AND UNSUPERVISED CLASSIFICATIONSupervised classification Advantages This image give us a visible picture by comparing both ground trooping and computer generation.Unsupervised Classification Advantages It might give us the image and classification of the site without inputting any ground trooping data, It will show the band representation with the three coordinator red, green and blue. It will distinguish the cell pixel density.