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Found 33 papers, 17 papers with code
Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection
In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention.
Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising
Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module.
Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction
To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models.
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification
It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework.
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy.
Ranked #1 on
Object Detection
on VEDAI
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention.
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet.
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
The IDE-block is used to characterize and aggregate the intradomain nonlocal relationships and the interdomain feature and distribution similarities are captured in the CSA-block.
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation
The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy.
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification
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.
A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification
The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing.
MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration
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.
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network
We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption.
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images
Important findings on the use of spatial and spectral information in the autoencoder framework are discussed.
Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification
Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue.
Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis.
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
Deep Learning for UAV-based Object Detection and Tracking: A Survey
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 Variability Augmented Sparse Unmixing of Hyperspectral Images
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.
Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network
Moreover, a correlation layer is designed to further explore the correlation between multitemporal images.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC.
Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
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.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
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
Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network
In addition, we further propose a multi-region convolution module, which emphasizes the central region of each patch.
Remote Sensing Image Translation via Style-Based Recalibration Module and Improved Style Discriminator
Then, a new style discriminator is designed to improve the translation performance.
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
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.
Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
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.
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network
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.
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image 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.
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
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).
Real-time Decolorization using Dominant Colors
Decolorization is the process to convert a color image or video to its grayscale version, and it has received great attention in recent years.
