Boosting the accuracy of multispectral image pansharpening by learning a deep residual network
In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the
impressive effectiveness of deep neural networks has recently been employed to overcome …
impressive effectiveness of deep neural networks has recently been employed to overcome …
Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images
Most current semantic segmentation approaches fall back on deep convolutional neural
networks (CNNs). However, their use of convolution operations with local receptive fields …
networks (CNNs). However, their use of convolution operations with local receptive fields …
Hybrid feature aligned network for salient object detection in optical remote sensing imagery
Recently, salient object detection in optical remote sensing images (RSI-SOD) has attracted
great attention. Benefiting from the success of deep learning and the inspiration of natural …
great attention. Benefiting from the success of deep learning and the inspiration of natural …
Lightweight salient object detection via hierarchical visual perception learning
Recently, salient object detection (SOD) has witnessed vast progress with the rapid
development of convolutional neural networks (CNNs). However, the improvement of SOD …
development of convolutional neural networks (CNNs). However, the improvement of SOD …
Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization
Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate
classification benefits a large number of applications such as land use analysis and marine …
classification benefits a large number of applications such as land use analysis and marine …
Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images
Ship detection in optical remote sensing imagery has drawn much attention in recent years,
especially with regards to the more challenging inshore ship detection. However, recent …
especially with regards to the more challenging inshore ship detection. However, recent …
Dual-clustering-based hyperspectral band selection by contextual analysis
Hyperspectral image (HSI) involves vast quantities of information that can help with the
image analysis. However, this information has sometimes been proved to be redundant …
image analysis. However, this information has sometimes been proved to be redundant …
Canonical correlation analysis networks for two-view image recognition
In recent years, deep learning has attracted an increasing amount of attention in machine
learning and artificial intelligence areas. Currently, many deep learning network-related …
learning and artificial intelligence areas. Currently, many deep learning network-related …
Video saliency detection using object proposals
In this paper, we introduce a novel approach to identify salient object regions in videos via
object proposals. The core idea is to solve the saliency detection problem by ranking and …
object proposals. The core idea is to solve the saliency detection problem by ranking and …
SPRNet: single-pixel reconstruction for one-stage instance segmentation
Object instance segmentation is one of the most fundamental but challenging tasks in
computer vision, and it requires the pixel-level image understanding. Most existing …
computer vision, and it requires the pixel-level image understanding. Most existing …