Few-shot image classification has attracted extensive attention, which aims to recognize
unseen classes given only a few labeled samples. Due to the large intraclass variances and …

Few-shot scene classification aims to recognize unseen scene concepts from few labeled
samples. However, most existing works are generally inclined to learn metalearners or …

This paper proposes a new method, called multilayer stacked covariance pooling (MSCP),
for remote sensing scene classification. The innovative contribution of the proposed method …

For the time being, there are many groundbreaking object detection frameworks used in
natural scene images. These algorithms have good detection performance on the data sets …

Remote sensing (RS) image scene classification is an important research topic in the RS
community, which aims to assign the semantics to the land covers. Recently, due to the …

This paper proposes a novel end-to-end learning model, called skip-connected covariance
(SCCov) network, for remote sensing scene classification (RSSC). The innovative …

Accurate urban land-use maps, which reflect the complicated land-use pattern implied in the
function and distribution of land-cover types, play an important role in urban analysis. In …

Hyperspectral image (HSI) enjoys great advantages over more traditional image types for
various applications due to the extra knowledge available. For the nonideal optical and …

Convolutional neural networks (CNNs) have achieved great success when characterizing
remote sensing (RS) images. However, the lack of sufficient annotated data (together with …

Deep neural networks, which can learn the representative and discriminative features from
data in a hierarchical manner, have achieved state-of-the-art performance in the remote …