Classification of Satellite Images Based on Color Features Using Remote Sensing

The aim of this paper is to classify satellite imagery using moment's features extraction with K-Means clustering algorithm in remote sensing. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. In this research, the study area chosen is to cover the area of Baghdad city in Iraq taken by landsat 8. The proposed work consists of two phases: training and classification. The training phase aims to extract the moment features (mean, standard deviation, and skewness) for each block of the satellite imagery and store as dataset used in classification phase to compute the similarity measurement.  The experimental result of classification showed that the image contains five distinct classes (rivers, agriculture area, buildings with vegetation, buildings without vegetation, and bare lands). The classification result assessment was carried out by comparing the result with a reference classified image achieved by Iraqi Geological Surveying Corporation (GSC). It is observed that both the user accuracy and producers' accuracy and hence overall classification accuracy are enhanced with percent 92.12447%

Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images

open6siThis paper presents an effective semiautomatic method for discovering and detecting multiple changes (i.e., different kinds of changes) in multitemporal hyperspectral (HS) images. Differently from the state-of-the-art techniques, the proposed method is designed to be sensitive to the small spectral variations that can be identified in HS images but usually are not detectable in multispectral images. The method is based on the proposed sequential spectral change vector analysis, which exploits an iterative hierarchical scheme that at each iteration discovers and identifies a subset of changes. The approach is interactive and semiautomatic and allows one to study in detail the structure of changes hidden in the variations of the spectral signatures according to a top-down procedure. A novel 2-D adaptive spectral change vector representation (ASCVR) is proposed to visualize the changes. At each level this representation is optimized by an automatic definition of a reference vector that emphasizes the discrimination of changes. Finally, an interactive manual change identification is applied for extracting changes in the ASCVR domain. The proposed approach has been tested on three hyperspectral data sets, including both simulated and real multitemporal images showing multiple-change detection problems. Experimental results confirmed the effectiveness of the proposed method.noneS. Liu; L. Bruzzone; F. Bovolo; M. Zanetti; P. DuS., Liu; L., Bruzzone; Bovolo, Francesca; M., Zanetti; P., Du; Zanetti, Massim