Registration for multisensor or multimodal image pairs with a large degree of distortions is fundamental task many remote sensing applications. To achieve accurate and low-cost registration, we propose multiscale framework unsupervised learning, named MU-Net. Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from to their transformation parameters. stacks several deep neural network (DNN) models on multiple scales generate coarse-to-fine registration pipeline,...

Most traditional approaches classify hyperspectral image (HSI) pixels relying only on the spectral values of input channels. However, spatial context around a pixel is also very important and can enhance classification performance. In order to effectively exploit fuse both structure, we propose novel two-stream deep architecture for HSI classification. The proposed method consists fusion scheme. architecture, one stream employs stacked denoising autoencoder encode each pixel, other takes as...

The supervised deep networks have shown great potential in improving the classification performance. However, training these is very challenging for hyperspectral image given fact that usually only a small amount of labeled samples are available. In order to overcome this problem and enhance discriminative ability network, paper, we propose network architecture super-resolution (SR)-aided with classwise loss (SRCL). First, three-layer SR convolutional neural (SRCNN) employed reconstruct...

Remote sensing (RS) image classification has attracted much attention recently and is widely used in various fields. Different to natural images, the RS scenes consist of complex backgrounds stochastically arranged objects, thus making it difficult for networks focus on target objects scene. However, conventional methods do not have any special treatment remote images. In this paper, we propose a two-stream swin transformer network (TSTNet) address these issues. TSTNet consists two streams...

Since Hyperspectral Images (HSIs) contain plenty of ground object information, they are widely used in fine-grain classification objects. However, some objects similar and the number spectral bands is far higher than categories. Therefore, it hard to deeply explore spatial–spectral joint features with greater discrimination. To mine HSIs, a Shallow-to-Deep Feature Enhancement (SDFE) model three modules based on Convolutional Neural Networks (CNNs) Vision-Transformer (ViT) proposed. Firstly,...

Variants of deep networks have been widely used for hyperspectral image (HSI)-classification tasks. Among them, in recent years, recurrent neural (RNNs) attracted considerable attention the remote sensing community. However, complex geometries cannot be learned easily by traditional units [e.g., long short-term memory (LSTM) and gated unit (GRU)]. In this article, we propose a geometry-aware network (Geo-DRNN) HSI classification. We build upon two modules: U-shaped (U-Net) RNNs. first input...

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based recognition focuses on exploiting temporal, spatial, spatiotemporal EEG data recognition. lack consideration both spatial temporal aspects data, accuracy detection algorithms employing solely or variables is low. In addition, approaches that use properties take characteristics into account; however, these methods extract...

To develop novel PI4KIIIβ inhibitors and explore their antitumor activity, a series of 5-phenylthiazol-2-amine derivatives were synthesized by structural modifications PIK93. Biological assay results indicated that compounds 16 43 exhibited superior selective inhibitory antiproliferative activity than Mechanistic studies revealed the two inhibit PI3K/AKT pathway more effectively, thereby inducing cancer cell apoptosis, cycle arrest in G2/M phase autophagy. Importantly, vivo toxicity...

Unmanned SystemsAccepted Papers No AccessJoint Terrestrial-Aerial Path Planning for Tensegrone RobotSongyuan Liu, Zhe Jing, Siyuan Hao, Jingshuo Lyu, Zichen Tao, Yun Gui, Hao Fang, and Qingkai YangSongyuan Jing Search more papers by this author , Lyu Tao Gui Fang Yang https://doi.org/10.1142/S2301385026500287Cited by:0 (Source: Crossref) PreviousNext AboutFiguresReferencesRelatedDetailsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend Library ShareShare...

Semi-supervised classification methods result in higher performance for hyperspectral images, because they can utilize the relationship between unlabeled samples and labeled to obtain pseudo-labeled samples. However, how generating an effective training sample set is a major challenge semi-supervised methods, In this paper, we propose novel method based on extended label propagation (ELP) rolling guidance filter (RGF) called ELP-RGF, which ELP new two-step process make full use of The first...

Convolutional neural networks (CNNs) have been widely used in remote sensing scene classification. However, the long-range dependencies of local features cannot be taken into account by CNNs. By contrast, a visual transformer (ViT) is good at capturing as it considers global relationship introducing self-attention mechanism. Although ViT can obtain result when training on large-scale datasets, e.g., ImageNet, hard to adapted small-scale datasets (e.g., image datasets). This attributed fact...

Due to their ability offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) are being used frequently in remote sensing tasks. However, as the number of views grows, issue quality is becoming apparent, limiting potential benefits data. Although recent deep neural network (DNN)-based models can learn weight adaptively, lack research on explicitly quantifying each view when fusing them renders these inexplicable,...

Keypoint detection is a crucial step for feature-based image registration. The traditional detectors only extract one type of keypoint such as corner or blob, which not quite beneficial to Accordingly, this letter presents novel detector that aims simultaneously corners and blobs. proposed named Harris-Difference Gaussian (DoG), combines the advantages Harris-Laplace DoG blob detector. In definition Harris-DoG, we first build an scale space by using multiscale Harris Then, these are screened...

Semisupervised learning has shown its great potential in land cover mapping. It exploits the information of unlabeled training samples and converts those to labeled enhance classification. In this paper, spatial extracted by a two-dimensional (2-D) Gabor filter was stacked with spectral first, then neighborhood combined active (AL) algorithm select most useful informative samples, which were used as set aid probability model-based supervised support vector machine (SVM). Experiments on two...

Phosphorodithioates are important substructures due to their great use in bioactive compounds and functional materials. A metal-free 1,5-addition of spirovinylcyclopropyl oxindoles have been developed by choosing P4S10 alcohol as nucleophiles through the regioselective ring-opening oxindoles. This method provides access allylic organothiophosphates with high efficiency, wide group tolerance, good chemo- regioselectivity, E-selectivity. 1,3-Addition products were also prepared yield....

In recent years, generative adversarial networks (GAN) have made great progress in the field of hyperspectral image classification (HIC), which alleviates problem insufficient training samples to a large extent. At present, GAN HIC are all based on Convolutional Neural Network (CNN). But CNN cannot extract sequence information well, and it is difficult model remote dependencies. However, hyperspectrum rich spectral information, Transformer has been proven be good at processing information....