Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

Recent Developments from Attribute Profiles for Remote Sensing Image Classification

Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task. Since their first introduction to this field in early 2010’s, many research studies have been contributed not only to exploit and adapt their use to different applications, but also to extend an...

Remote Sensing Image Classification

Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...

Remote Sensing Image Classification Based on Gray System Theory

The intelligence and automation of image processing and analysis is a bottle problem for photogrammetry and remote sensing. Artificial neural networks is a new solver which imitates brain and gray system theory is a new tool which handles undetermined problem. This paper describe how to combine artificial neural networks with gray system theory to realize classification of remote sensing image ...

Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised ...