In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

In recent years, the combination of deep learning and remote sensing has been a boiling
state. However, because of the difference between remote sensing images and natural …

Because of its wide potential applications, remote sensing (RS) image semantic
segmentation has attracted increasing research interest in recent years. Until now, deep …

Although the deep semantic segmentation network (DSSN) has been widely used in remote
sensing (RS) image semantic segmentation, it still does not fully mind the spatial …

Image captioning, ie, generating the natural semantic descriptions of given image, is an
essential task for machines to understand the content of the image. Remote sensing image …

Unsupervised hashing is an important approach for large-scale content-based remote
sensing (RS) image retrieval. Existing unsupervised hashing methods usually utilize data …

Deep learning methods have recently found widespread adoption for remote sensing tasks,
particularly in image or pixel classification. Their flexibility and versatility has enabled …

Land cover and its dynamic information is the basis for characterizing surface conditions,
supporting land resource management and optimization, and assessing the impacts of …

In practical applications, different application fields have various requirements regarding the
precision and completeness of building extraction. Too low precision or completeness may …

In this paper, we propose a new deep network architecture named deep boosting denoising
net (DBDnet) for image denoising. It is a residual learning network that can generate a noise …