Deep learning for remote sensing image classification:A survey

Remote sensing (RS) image classification plays an important role in the earth observation technology using RS data, having been widely exploited in both military and civil fields. However, due to the characteristics of RS data such as high dimensionality and relatively small amounts of labeled samples available, performing RS image classification faces great scientific and practical challenges. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image classification. In this paper, a brief overview of typical DL models is presented first. This is followed by a systematic review of pixel?wise and scene?wise RS image classification approaches that are based on the use of DL. A comparative analysis regarding the performances of typical DL?based RS methods is also provided. Finally, the challenges and potential directions for further research are discussedpublishersversionPeer reviewe

Extinction Profiles Fusion for Hyperspectral Images Classification

An extinction profile (EP) is an effective spatial-spectral feature extraction method for hyperspectral images (HSIs), which has recently drawn much attention. However, the existing methods utilize the EPs in a stacking way, which is hard to fully explore the information in EPs for HSI classification. In this paper, a novel fusion framework termed EPs-fusion (EPs-F) is proposed to exploit the information within and among EPs for HSI classification. In general, EPs-F includes the following two stages. In the first stage, by extracting the EPs from three independent components of an HSI, three complementary groups of EPs can be constructed. For each EP, an adaptive superpixel-based composite kernel strategy is proposed to explore the spatial information within an EP. The weights to create the composite kernel and the number of superpixels are automatically determined based on the spatial information of each EP. In the second stage, since the different EPs contain highly complementary information, a simple yet effective decision fusion method is further applied to obtain the final classification result. Experiments on three real HSI data sets verify the qualitative and quantitative superiority of the proposed EPs-F method over several state-of-the-art HSI classifiers