Remote Sensing Image Classification Using the Spectral-Spatial Distance Based on Information Content

A Content-Based Remote Sensing Image Change Information Retrieval Model

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-b...

The Classification Method of Multi-spectral Remote Sensing Images Based on Self-adaptive Minimum Distance Adjustment

The phenomenon of “Same Object with Different Spectra” in the issue of multi-spectral remote sensing images land use classification makes major effects on improving accuracy. The paper based on the analysis of modeling on classification problems, proposed a method based on minimum distance self-adaptive adjustment to realize the split of cluster centers and solved the problem of identified scop...

Spatial-spectral Classification Based on the Unsupervised Convolutional Sparse Auto-encoder for Hyperspectral Remote Sensing Imagery

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature informati...

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...