[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
SpectralGPT: Spectral remote sensing foundation model
The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …
revolutionize the field of visual representation learning in a self-supervised manner. While …
SpectralFormer: Rethinking hyperspectral image classification with transformers
Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …
information, enabling the fine identification of materials by capturing subtle spectral …
Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification
Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …
Convolutional neural networks for multimodal remote sensing data classification
In recent years, enormous research has been made to improve the classification
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
performance of single-modal remote sensing (RS) data. However, with the ever-growing …
Multimodal fusion transformer for remote sensing image classification
Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …
promising performance when compared with convolutional neural networks (CNNs). As a …
Rotation-invariant attention network for hyperspectral image classification
Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …
based on spectral signatures and spatial information of HSIs. In recent deep learning-based …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Learning tensor low-rank representation for hyperspectral anomaly detection
Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …