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Hyperspectral scene classification dataset based on Zhuhai-1 images

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：liuyuan213@mails.ucas.ac.cn
  • Introduction：刘渊，研究方向为遥感图像数据处理。E-mail： liuyuan213@mails.ucas.ac.cn
LIU Yuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

  • Email：xiangtaoz@gmail.com
  • Introduction：郑向涛，研究方向为遥感图像处理。E-mail： xiangtaoz@gmail.com
ZHENG Xiangtao3*,  
  • Affiliation：

    College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

LU Xiaoqiang3

résumé

Hyperspectral remote sensing is a key technology for remotely obtaining the physical parameters of ground objects and realizing fine identification. It can not only get geometrical properties of the target scenes but also obtain radiance that reflects the characteristics of ground objects. With the development of hyperspectral remote sensing data to unprecedented spatial, spectral, temporal resolution and large data volume, how to adapt to the requirements of massive data and achieve efficient and rapid processing of hyperspectral remote sensing data has become the current research focus. Researchers are introducing scene classification into hyperspectral image classification, integrating the spatial and spectral information to obtain semantic information oriented to larger observation units. However, almost all existing multispectral/hyperspectral scene classification datasets have a number of limitations, including inconsistent spectral and spatial resolutions or spatial resolutions too large to meet the needs of fine-grained classification. Based on the hyperspectral images of Xi’an taken by the “Zhuhai-1” constellation, we combine the result of unsupervised spectral clustering and Google Earth to establish a hyperspectral satellite image scene classification dataset named HSCD-ZH (Hyperspectral Scene Classification Dataset from Zhuhai-1). It consists of 737 images divided into six categories: urban, agriculture, rural, forest, water, and unused land. Each image with a size of 64×64 pixels consists of 32 bands covering the wavelength in the range of 400—1000 nm. In addition, we conduct spatial-based and spectral-based experiments to analyze the performance of existing datasets, and the benchmark results are reported as a valuable baseline for subsequent research. We choose false-color image for the spatial-based experiments and then use popular deep and non-deep learning scene classification techniques. In the experiments based on spectral, the spectral vectors at the pixel are directly used as local spectral features, and BoVW, IFK, and LLC are used to encode them to generate global representations for the scene. Using SVM as the classifier, the optimal overall classification achieved by the two experiments on the proposed dataset is 92.34% and 88.96%, respectively. Considering that those methods have a large amount of information loss, we cascade the features extracted by the two approaches to generate spatial-spectral features. The highest overall accuracy obtained reaches 94.64%, which is the highest improvement in overall accuracy compared to the other datasets. We construct HSCD-ZH by effectively exploiting both spectral and spatial features of hyperspectral images, selecting various scenes that either have representative spectral compositions, clear spatial textures, or both. It has the advantages of big intraclass diversity, strong scalability, and adapting to satellite hyperspectral intelligent information extraction requirements. Both dataset and experiments can provide effective data support for remote sensing scene classification research in the hyperspectral field. Meanwhile, experiments can indicate that extracting features based on spatial or spectral misses a large amount of available information, and integrating the features extracted by the two methods can compensate for this deficiency. In our future work, we aim to expand the number of categories and images of HSCD-ZH and continue to explore algorithms for integrating spatial and spectral information that can accelerate the interpretation and efficient exploitation of hyperspectral scene cubes.

mots-clés

hyperspectral remote sensing;Zhuhai-1;scene classification;dataset;feature extraction

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