IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION

Remote Sensing Image Classification

Remote Sensing Image Classification using Back Propogation

The resolution of remote sensing images increase every day . Most of the existing methods is used the same method for years. The existing method does not provide satisfactory result. The aim is to develop an artificial neural network based on classification method consists of segmentation and classification . Segmentation followed by K-Means method and then classification performed with back pr...

Automatic Approach to Vhr Satellite Image Classification

In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initia...

Support Vector Machines for Remote-Sensing Image Classification

In the last decade, the application of statistical and neural network classifiers to remote-sensing images has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from remote-sensing practitioners. In this paper, we present the application to remote-sensing image classification of a new pattern recognition techniqu...

Effective Sequential Classifier Training for Multitemporal Remote Sensing Image Classification

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study...