Satellite Image Classification Using K-Means and SVD Techniques

The use of remote sensing technologies was gained more attention due to an increasing need to collect data for the environmental dynamics.  Digital image analysis has been used for forest mapping and inventory evaluations, and it has played a substantial role in landcover monitoring and assessment. Satellite image classification is a relatively recent type of remote sensing uses satellite imagery to
indicate many key environment characteristics.The motivation of current research is addressed at establishing a satellite image classification based on Singular Value Decomposition (SVD) technique. In
addition to the preprocessing stages, the proposed classification method is designed to be consisted of two phases: enrollment and classification. It was intended for the proposed method to be used the SVD and applied on multispectral satellite image,the used image is pictured by Landsat satellite for Baghdad city in Iraq with medium resolution of 1024 1024pixel. The enrollment phase aims at extracting the image classes to be stored in dataset as a training data. Since the SVD method is supervised method, it cannot enroll the intended dataset, instead, the moment based K-means was used to build the dataset. Thereby, the enrollment phase began with partitioning the image into uniform sized blocks, and estimating the moment for each image block. The moment is the feature by which the image blocks were grouped. Then, K-means is used to cluster the image blocks and determining the number of cluster and centroid of each cluster. The image block corresponding to these centroids were stored in the dataset to be used in the classification phase.The enrollment phase showed that the image contains five distinct classes:
water, vegetation, residential without vegetation, residential with vegetation, and open land. Therefore, a specific image block of each class stored in dataset to be act as training area. These training areas pointed on the image and monitored to validate the available information of classes.SVD based classification method required to be consisted of multi stages,these are: image composition, image transform, image partitioning, feature extraction, and then image classification. The image partitioning stage was carried out by quadtree method. The practice and analysis showed there were many effective conditions affect image partitioning, the analysis help to achieve the best values of such conditions to get best results. The proposed classification method used the dataset to estimate the classification feature of SVD and compute the similarity measure for each block in the image. The similarity measure indicates the class of the dataset that the image block was belongs to. Frequently, the image is classified block by block and then the classified image is displayed.After assurance the acceptable behavior of the classification that basically depends of the dense information found in the dataset, the classification path is
modified to classify the image pixel by pixel. In such situation, each pixel value is compared with the mean value of each class in the dataset, and a similarity measure is computed between them, which indicate the class that the pixel belongs to.Accordingly, the resulted image was showed good classification. The results assessment was carried out on the two classification paths by comparing the results with a reference classified image achieved by Iraqi Geological Surveying Corporation (IGSC). The comparison process is done pixel by pixel for whole the considered image and computing some evaluation
measurements. It was found that the classification method was high quality performed and the results showed acceptable classification scores of about 70.64%,and it is possible to be approaches 81.833% when considering both classes:
residential without vegetation and residential with vegetation as one class for SVD method. Also, the classification score was about 95.84% for the moment method.This indicates the ability of proposed methods to efficient classify multibands satellite image. The encouraging results gave the chance to suggest development problems for the present work to be better in the future when using another classification features for strengthing the classification results.