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Found 18 papers, 7 papers with code
Toward distortion-aware change detection in realistic scenarios
The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning.
CLIP-guided Source-free Object Detection in Aerial Images
Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data.
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.
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective
To address this issue, we propose a novel TN paradigm, named SVD-inspired TN decomposition (SVDinsTN), which allows us to efficiently solve the TN-SS problem from a regularized modeling perspective, eliminating the repeated structure evaluations.
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Object detection in remote sensing images relies on a large amount of labeled data for training.
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.
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.
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).
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.
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.
SAR Image Change Detection Based on Multiscale Capsule Network
On the one hand, the multiscale capsule module is employed to exploit the spatial relationship of features.
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.
SAR Image Change Detection Based on Multiscale Capsule Network
On the one hand, the capsule module is employed to exploit the spatial relationship of features.
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.
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.
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
However, there is a limitation in current deep learning based ATR solution that each learning process only handle one SAR image, namely learning the static scattering information, while missing the space-varying information.
