17 datasets found

The Indian Pines 2010 dataset is a 2m resolution image taken over the Indian Pines test site in May 2010.

Hyperspectral remote sensing images have high dimensionality and a large number of channels with substantial redundancy between channels. The training data for classifying HSRS...

The Indian Pines dataset contains 16 land cover types, which leaves 200 bands to be used for experiments after removing noise and water absorption bands.

Hyperspectral remote sensing images have high dimensionality and a large number of channels with substantial redundancy between channels. The training data for classifying HSRS...

The dataset used in this paper is a hyperspectral image dataset, which is a type of multi-dimensional data. The dataset is used for tensor completion, a problem in scientific...

This dataset contains 50 HS images of spatial dimensions 1040 × 1392 and spectral dimension 31.

The NTIRE2018 dataset is acquired by a Specim PS Kappa DX4 hyperspectral camera.

The NUS dataset is captured by a Specim’s PFDCL-65-V10E spectral camera.

The CAVE dataset is comprised of 32 scenes of a wide range of materials and objects, such as skin, fruits, drinks, feathers, paintings, etc.

Hyperspectral image super-resolution (HSI SR) task is critical and meaningful to enhance the image quality to better serve the subsequent high-level computer vision tasks.

The Pavia University dataset was acquired by ROSIS airborne sensor over the University of Pavia, Italy, in 2003. The original HSI comprises 610×340 pixels and 115 spectral bands.

The Legendre Dataset is a set of hyperspectral synthetic images from the IC Synthetic Hyperspectral Collection.

University of Pavia is a popular benchmark dataset for semantic segmentation.

Indian Pines and Salinas-A hyperspectral image datasets used for band selection evaluation

The dataset used in this paper is a hyperspectral image with 190 pixels and 188 channels, covering a region of 250x250 pixels with 14 types of minerals.

The dataset used in the paper is a collection of digital breast tomosynthesis (DBT) stacks with masses inserted in the center of the stack.

The Salinas dataset was acquired over the Salinas Valley in California and gathered by the AVIRIS sensor.