A Survey of Satellite High Resolution Image Classification

Abstract

The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process Image classification is the most important process in pattern reorganization. Image classification is one of the most complex areas in image processing. It is more complex and difficult to classify if it contain blurry and noisy content. There are several methods to classify images and they provide good classification result but they fail to provide satisfactory classification result when the image contains blurry and noisy content. The two main methods for image classification are supervised and unsupervised classification. In general, this is a final stage of pattern matching. The classification process described the percentage of accuracy in pattern recognition. Feature extraction is another vital stage in pattern matching. These extracted feature are used for classification of the image database, that is pattern matching.

Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

“…The algorithm for semantic annotation module is explained in Algorithm 1.In this algorithm, the UC Merced image data set is taken as input and the output of this algorithm is Set of annotated class labels [25]. Initially, in this algorithm Image Set (IS) is defined as set of images ranging from IS 1 to IS n .…”