WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification

• URL: http://arxiv.org/abs/2012.13920v2
• Date: Tue, 30 Mar 2021 10:42:58 GMT
• Title: WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification
• Authors: Xin Hu, Yanfei Zhong, Chang Luo, Xinyu Wang
• Abstract summary: A new benchmark dataset named the Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset was built for hyperspectral image classification. The WHU-Hi dataset has a high spectral resolution (nm level) and a very high spatial resolution (cm level) Some start-of-art hyperspectral image classification methods benchmarked the WHU-Hi dataset, and the experimental results show that WHU-Hi is a challenging dataset.
• Score: 5.281167336437183
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classification is an important aspect of hyperspectral images processing and application. At present, the researchers mostly use the classic airborne hyperspectral imagery as the benchmark dataset. However, existing datasets suffer from three bottlenecks: (1) low spatial resolution; (2) low labeled pixels proportion; (3) low degree of subclasses distinction. In this paper, a new benchmark dataset named the Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset was built for hyperspectral image classification. The WHU-Hi dataset with a high spectral resolution (nm level) and a very high spatial resolution (cm level), which we refer to here as H2 imager. Besides, the WHU-Hi dataset has a higher pixel labeling ratio and finer subclasses. Some start-of-art hyperspectral image classification methods benchmarked the WHU-Hi dataset, and the experimental results show that WHU-Hi is a challenging dataset. We hope WHU-Hi dataset can become a strong benchmark to accelerate future research.

Related papers

• Rethinking Image Super-Resolution from Training Data Perspectives [54.28824316574355]
  We investigate the understudied effect of the training data used for image super-resolution (SR) With this, we propose an automated image evaluation pipeline. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance.
  arXiv  Detail & Related papers  (2024-09-01T16:25:04Z)
• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [64.72242126879503]
  Hyperspectral images (HSI) clustering is an important but challenging task. We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI. We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification [57.1795052451257]
  We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples. We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems. The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
  arXiv  Detail & Related papers  (2024-01-26T08:28:13Z)
• Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering [4.643572021927615]
  This paper introduces a novel unsupervised HSI clustering algorithm, Superpixel-based and Spatially-regularized Diffusion Learning (S2DL) S2DL incorporates rich spatial information encoded in HSIs into diffusion geometry-based clustering. S2DL's performance is illustrated with extensive experiments on three publicly available, real-world HSIs.
  arXiv  Detail & Related papers  (2023-12-24T09:54:40Z)
• A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data [5.1901440366375855]
  The use of Deep Learning techniques for classification in Hyperspectral Imaging (HSI) is rapidly growing. Recent methods have also explored the usage of Graph Convolution Networks and their unique ability to use node features in prediction.
  arXiv  Detail & Related papers  (2023-10-16T00:42:25Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information [0.0]
  This paper investigates the performance of four supervised learning algorithms, namely, Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and Linear Discriminant Analysis LDA. The experiments have been performed on three real hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging Spectrometer ROSIS sensors.
  arXiv  Detail & Related papers  (2022-10-27T13:39:08Z)
• 3D Visualization and Spatial Data Mining for Analysis of LULC Images [0.0]
  The present study is an attempt made to create a new tool for the analysis of Land Use Land Cover (LUCL) images in 3D visualization. This study mainly uses spatial data mining techniques on high resolution LULC satellite imagery.
  arXiv  Detail & Related papers  (2022-01-28T07:51:31Z)
• Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction [64.10636296274168]
  Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectral and spatial resolution. Recently proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (ConvNets) We propose a novel over-complete network, called HyperKite, which focuses on learning high-level features by constraining the receptive from increasing in the deep layers.
  arXiv  Detail & Related papers  (2021-07-06T14:11:03Z)
• Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets [99.92564298432387]
  This work studies Hyperspectral image (HSI) super-resolution (SR) HSI SR is characterized by high-dimensional data and a limited amount of training examples. This exacerbates the undesirable behaviors of neural networks such as memorization and sensitivity to out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-01-19T12:19:53Z)
• Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network [2.9967206019304937]
  Hyperspectral image (HSI) classification is a standard remote sensing task, in which each image pixel is given a label indicating the physical land-cover on the earth's surface. The utilization of both the spectral and spatial cues in hyperspectral images has shown improved classification accuracy in hyperspectral image classification. The use of only 3D Convolutional Neural Networks (3D-CNN) to extract both spatial and spectral cues from Hyperspectral images results in an explosion of parameters hence high computational cost. We propose network architecture called the MixedSN that utilizes the 3D convolutions to modeling spectral-spatial information
  arXiv  Detail & Related papers  (2020-02-07T01:54:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.