Hyperspectral scene classification dataset based on Zhuhai-1 images
- Vol. 28, Issue 1, Pages: 306-319(2024)
Published： 07 January 2024
DOI： 10.11834/jrs.20233283
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摘要
高空间分辨率、高光谱分辨率、大幅宽与大数据量是高光谱卫星数据发展趋势，传统高光谱影像的像素级分类面临难以处理海量数据、无法高效获取复杂海量影像中隐含信息的困境。已有研究开始关注高光谱影像的场景级分类，并逐步建立完善高光谱遥感场景分类数据集。然而，目前的数据集制作过程多参考高空间分辨率可见光遥感场景数据集的制作方法，主要采用遥感影像的空间信息进行场景类别解译，忽视了高光谱场景的光谱信息。因此，为构建高光谱影像的遥感场景分类数据集，本文利用“珠海一号”高光谱卫星拍摄的西安地区高光谱数据，使用无监督光谱聚类辅助定位、裁剪与标注待选场景样本，结合Google Earth高分影像进行目视筛选，构建6类场景类型和737幅场景样本的珠海一号高光谱场景分类数据集。并基于光谱与空间两个视角开展场景分类实验，通过视觉词袋、卷积神经网络等方法的基准测试结果，对不同算法在现有多光谱和高光谱遥感场景分类数据集下的性能进行深入分析。本研究可为后续的高光谱影像解译研究提供了有力的数据支撑。
Abstract
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
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https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=54409759&type=
https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=54409757&type=
8.63599968
2.11666679
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.
关键词
高光谱遥感珠海一号场景分类数据集特征提取
Keywords
hyperspectral remote sensingZhuhai-1scene classificationdatasetfeature extraction
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School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology
State Key Laboratory of Remote Sensing Science, Beijing Normal University
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
School of Earth Sciences and Engineering, Hohai University
School of Earth Science and Engineering, Hohai University
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