Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

Context guided belief propagation for remote sensing image classification.

We propose a context guided belief propagation (BP) algorithm to perform high spatial resolution multispectral imagery (HSRMI) classification efficiently utilizing superpixel representation. One important characteristic of HSRMI is that different land cover objects possess a similar spectral property. This property is exploited to speed up the standard BP (SBP) in the classification process. Sp...

Remote Sensing Image Classification

A Modified Stochastic Neighbor Embedding for Combining Multiple Features for Remote Sensing Image Classification

In remote sensing image interpretation, it is important to combine multiple features of a certain pixel in both spatial and spectral domains to improve the classification accuracy, such as spectral signature, morphological property, and shape feature. Therefore, it is essential to consider the complementary property of different features and combine them in order to obtain an accurate classific...

Remote Sensing Image Dehazing using Guided Filter

Remote sensing image dehazing is challenging because it is massively ill-posed and the haze is dependent on the unknown depth information. Haze removal based on dark channel prior is effective, and refining the transmission map with Gaussian filter will produce a god result. But need to improve the naturalness and sharpness and effectiveness of images and to remove fine haze . So Proposes new a...

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...