The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy.
Specifically, Sa-GCN and Se-GCN are proposed to extract the spatial and spectral features by modeling correlations between spatial pixels and between spectral bands, respectively.
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing.
Considering the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm named Multi-scale Histogram of Local Main Orientation (MS-HLMO) is proposed.
We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption.
Important findings on the use of spatial and spectral information in the autoencoder framework are discussed.
Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue.
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis.
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS).
Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis.
Moreover, a correlation layer is designed to further explore the correlation between multitemporal images.
In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC.
Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels.
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Ranked #1 on 2D Semantic Segmentation on xBD
In addition, we further propose a multi-region convolution module, which emphasizes the central region of each patch.
Then, a new style discriminator is designed to improve the translation performance.
In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.
Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.
In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs.
In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD).