A General Self-Supervised Framework for Remote Sensing Image Classification

Remote Sensing Image Classification

Deep Self-taught Learning for Remote Sensing Image Classification

This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our selftaught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple layers and use the output of the deepest layer ...

Fuzzy Supervised Classification of Remote Sensing Images

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of informatio...

Remote Sensing and Land Use Classification: Supervised vs. Unsupervised Classification

In this time of large-scale planning and land management on public lands, managers are increasingly looking for faster and less expensive methods of data collection. In efforts to make better decisions, planners need to be able to look at changes over time to assess trends. Policy makers are also looking to assess the effects of policies such as prescribed fire or fire suppression. All of these...

Mixture Distributions for Weakly Supervised Classification in Remote Sensing Images

For its simplicity and efficiency, the bag-of-words representation based on appearance features is widely used in image and text classification. Its drawback is that shape patterns of the image are neglected. This paper presents a novel image classification approach using a bag-of-words representation of textons while taking into account spatial information. A generative probabilistic modeling ...