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Found 6 papers, 1 papers with code
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
Hyperspectral imaging provides precise classification for land use and cover due to its exceptional spectral resolution.
Land use/land cover classification of fused Sentinel-1 and Sentinel-2 imageries using ensembles of Random Forests
To overcome these issues, ensembles of random forests (RFE) are created, introducing random rotations using the Forest-RC algorithm.
Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck
In this research, we deal with the problem of visual question answering (VQA) in remote sensing.
Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework
We tackle the problem of image inpainting in the remote sensing domain.
Two Headed Dragons: Multimodal Fusion and Cross Modal Transactions
The model is composed of stacked auto encoders that harness the cross key-value pairs for HSI and LiDAR, thus establishing a communication between the two modalities, while simultaneously using the CNNs to extract the spectral and spatial information from HSI and LiDAR.
