Satellite Image Classification Using Moment and SVD Method
The motivation we address in this paper is to classify satellite image using the moment and singular value decomposition (SVD) method; both proposed methods are consisted of two phases; the enrollment and classification. The enrollment phase aims to extract 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 results of enrollment phase showed that the image contains five distinct classes, they are; water, vegetation, residential without vegetation, residential with vegetation, and open land. The classification phase consisted of multi stages; image composition, image transform, image partitioning, feature extraction, and then image classification. The SVD classification method used the dataset to estimate the classification feature SVD and compute the similarity measure for each block in the image, while the moment classification method used the dataset to compute the mean of each column and compute the similarity measure for each pixel in the image. 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 (GSC). 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. In the SVD method, the score was about 70.64%, and it is possible to rise up to 81.833% when assuming both classes: residential without vegetation and residential with vegetation are one class.
Whereas, the classification score was about 95.84% when using the moment method. This encourage results indicates the ability of proposed methods to efficient classifying multibands satellite image.
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