Over the last two decades, multiple classifier system (MCS) or classifier ensemble has
shown great potential to improve the accuracy and reliability of remote sensing image …

Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs)
have shown excellent performances for classifying hyperspectral remote sensing …

Scene classification from remote sensing images provides new possibilities for potential
application of high spatial resolution imagery. How to efficiently implement scene recognition …

In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral
remote sensing image classification for the first time. The framework of Rotation Forest …

Classification is one of the most important techniques to the analysis of hyperspectral remote
sensing images. Nonetheless, there are many challenging problems arising in this task. …

In order to investigate the impacts of different information fusion techniques on change
detection, a sequential fusion strategy combining pan-sharpening with decision level fusion is …

As a result of urbanization, land use/land cover classes in urban areas are changing rapidly,
and this trend increased in the recent years. Change information detected from multi-…

In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized
by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and …

To obtain accurate classification results of hyperspectral images, both spectral and spatial
information should be fully exploited in the classification process. In this paper, we propose a …