Improved Connected Component Labeling Algorithm for Remote Sensing Image Classification

Remote Sensing Image Classification

Remote Sensing Image Classification Based on Improved Fast Independent Component Analysis

The increasing requirement of classification categories is followed by the increasing probabilities of wrong classification and the decreasing classification speed. If we can separate certain types of pixels out in advance, and then classify the remaining pixels, we can reduce the probabilities of mistakes effectively. This paper proposed an improved Fast Independent Component Analysis (ICA) ba...

Remote Sensing Image Classification of the Improved BP NN

Xiangwei Liu Foundation Department, PLA University of Foreign Languages, Luoyang 471003, China E-mail: [email protected] Abstract Remote sensing (RS) image classification plays the very important practical role in the geological survey and mineral exploration. The neural network (NN) technology is an important method for the RS image classification. But, the BP NN still has shortcomings, for ...

Bayesian Labeling of Remote Sensing Image Content

In this paper we present a multi-level scheme for stochastic description of image content. The diierent levels are derived from the diierent degrees of abstraction. On the level of the image data, we use stochastic data models and Bayesian parameter estimation to derive low-level image features. On the next level, we derive meta features that provide both the t of these models and the actual co...

Parallelized Dilate Algorithm for Remote Sensing Image

As an important algorithm, dilate algorithm can give us more connective view of a remote sensing image which has broken lines or objects. However, with the technological progress of satellite sensor, the resolution of remote sensing image has been increasing and its data quantities become very large. This would lead to the decrease of algorithm running speed or cannot obtain a result in limited...