• Scopus
  • Chinese Science Citation Database (CSCD)
  • A Guide to the Core Journal of China
  • CSTPCD
  • F5000 Frontrunner
  • RCCSE
Advanced search
Zhang Ying, Zhang Xiaoli, Li Hongzhi, Liu Huiling. A comparative analysis on hyperspectral land-cover classification based on frequency spectrum and spectral characteristics[J]. Journal of Beijing Forestry University, 2018, 40(7): 1-8. DOI: 10.13332/j.1000-1522.20170342
Citation: Zhang Ying, Zhang Xiaoli, Li Hongzhi, Liu Huiling. A comparative analysis on hyperspectral land-cover classification based on frequency spectrum and spectral characteristics[J]. Journal of Beijing Forestry University, 2018, 40(7): 1-8. DOI: 10.13332/j.1000-1522.20170342

A comparative analysis on hyperspectral land-cover classification based on frequency spectrum and spectral characteristics

More Information
  • Received Date: September 24, 2017
  • Revised Date: April 17, 2017
  • Published Date: June 30, 2018
  • ObjectiveFrequency spectrum as an energy feature of matter can be used to differentiate ground object recognition. However, the study for classification by frequency spectrum is scanty at home and abroad, especially for forest vegetation classification research. This study explored the potential of frequency spectrum in identifying the objects by converting optical spectrum of image into frequency spectrum.
    MethodFor exploring frequency spectrum in land-cover classification, the optical spectrum was converted to frequency spectrum to classify based on the fused hyperspectral images from domestic HJ-1A HSI and CCD data by CN Spectral Sharpening in farmland of Jiangle County, Sanming City of Fujian Province, southern China. Then the distance was developed to differentiate the objects based on the obtained frequency spectrum. The classification result based on frequency spectrum was compared with spectral angle mapping (SAM) based on spectrum space.
    ResultThe study result showed that the separability was clear among frequency spectrum of different ground objects. The frequency spectrum of the vegetation was significantly different with frequency spectrum of non-vegetation classed, and the spectrum of different tree species can also be differentiated in the low frequency region. The precisions for Pinus massoniana, Cunninghamia lanceolata and broadleaved forest were improved based on the FSSM method. The non-vegetation classes can be distinguished using frequency spectrum of first order harmonic. The vegetation classes can be distinguished using frequency spectrum from one to seven harmonic. However, the frequency spectrum for all objects tends to be similar with the increase of frequency. And the energy accumulated speed was faster for non-vegetation classes. Compared with SAM result, the overall classification precision from frequency spectrum method increased by 0.7%, which was 84.19%.
    ConclusionThe comparison results from frequency spectrum of different land types and classification results indicated that the frequency spectrum can be efficiently applied to identify objects. This method can reduce the influence of the noise from spectrum curve and keep the important distinction information between the classes. So, the frequency spectrum can be used for object identification.
  • [1]
    王珂, 顾行发, 余涛, 等.基于频谱相似性的高光谱遥感图像分类方法[J].中国科学(技术科学), 2013, 43(4): 407-416. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgkx-ce201304008

    Wang K, Gu X F, Yu T, et al. Classification of hyperspectral remote sensing images using frequency spectrum similarity[J]. Scientia Sinica (Technologica), 2013, 43(4): 407-416. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgkx-ce201304008
    [2]
    Thenkabail P S, Enclona E A, Ashton M S, et al. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests[J]. Remote Sensing of Environment, 2004, 90(1): 23-43. doi: 10.1016/j.rse.2003.11.018
    [3]
    李娜, 李咏洁, 赵慧洁, 等.基于光谱与空间特征结合的改进高光谱数据分类算法[J].光谱学与光谱分析, 2014, 34(2): 526-531. doi: 10.3964/j.issn.1000-0593(2014)02-0526-06

    Li N, Li Y J, Zhao H J, et al. An improved classification approach based on spatial and spectral features for hyperspectral data[J]. Spectroscopy and Spectral Analysis, 2014, 34(2): 526-531. doi: 10.3964/j.issn.1000-0593(2014)02-0526-06
    [4]
    张帆, 杜博, 张良培, 等.一种结合波段分组特征和形态学特征的高光谱图像分类方法[J].计算机科学, 2014, 41(12): 275-279. doi: 10.11896/j.issn.1002-137X.2014.12.059

    Zhang F, Du B, Zhang L P, et al. Band grouping based hyperspectral image classification using mathematical morphology and support vector machines[J]. Computer Science, 2014, 41(12): 275-279. doi: 10.11896/j.issn.1002-137X.2014.12.059
    [5]
    浦瑞良, 宫鹏, 约翰R.米勤.美国西部黄松叶面积指数与高光谱分辨率CASI数据的相关分析[J].环境遥感, 1993, 8(2): 112-125.

    Pu R L, Gong P, Miqin J R, et al. Correlating leaf area index of Ponderosa pine with hyperspectral CASI data[J]. Remote Sensing of Environment China, 1993, 8(2): 112-125.
    [6]
    Dian Y Y, Li Z Y, Pang Y. Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery[J]. Journal of the Indian Society of Remote Sensing, 2015, 43(1): 101-107. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=b6f347e54ac90834031b325496a20dab
    [7]
    Ghiyamat A, Shafri H Z M, Mahdiraji G A, et al. Airborne hyperspectral discrimination of tree species with different ages using discrete wavelet transform[J]. International Journal of Remote Sensing, 2015, 36(1): 318-342. doi: 10.1080/01431161.2014.995272
    [8]
    柴颖, 阮仁宗, 傅巧妮.高光谱数据湿地植被类型信息提取[J].南京林业大学学报(自然科学版), 2015, 39(1): 181-184. http://d.old.wanfangdata.com.cn/Periodical/njlydxxb201501033

    Chai Y, Ruan R Z, Fu Q N. Extraction of wetland vegetation information using hyperspectral image data[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2015, 39(1): 181-184. http://d.old.wanfangdata.com.cn/Periodical/njlydxxb201501033
    [9]
    李双, 徐新良, 付颖.基于高光谱影像的三江源区不同退化程度高寒草甸分类研究[J].遥感技术与应用, 2015, 30(1): 50-57. http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201501007

    Li S, Xu X L, Fu Y. A study on classification of different degradation level alpine meadows based on hyperpectral image data in three-river headwater region[J]. Remote Sensing Technology and Application, 2015, 30(1): 50-57. http://d.old.wanfangdata.com.cn/Periodical/ygjsyyy201501007
    [10]
    童庆禧, 唐川, 励惠国.腾冲航空遥感试验推陈出新[J].地球信息科学, 1999(1): 67-75. doi: 10.3969/j.issn.1560-8999.1999.01.017

    Tong Q X, Tang C, Li H G. A creative action:the second experiment of Teng Chong aerial remote sensing[J]. Geo-information Science, 1999(1): 67-75. doi: 10.3969/j.issn.1560-8999.1999.01.017
    [11]
    顾娟, 李新, 黄春林.基于时序MODIS NDVI的黑河流域土地覆盖分类研究[J].地球科学进展, 2010, 25(3): 317-326. http://d.old.wanfangdata.com.cn/Periodical/dqkxjz201003010

    Gu J, Li X, Huang C L. Land cover classification based on time series MODIS NDVI data in Heihe River Basin[J]. Advances of Earth Science, 2010, 25(3): 317-326. http://d.old.wanfangdata.com.cn/Periodical/dqkxjz201003010
    [12]
    除多, 边巴次仁, 王伟, 等.利用离散傅立叶变换分析拉萨地区植被季节变化特征[J].山地学报, 2010, 28(5): 579-585. doi: 10.3969/j.issn.1008-2786.2010.05.011

    Chu D, Bianbaciren, Wang W, et al. Vegetation ponologies in Lhasa area using the discrete fourier transform[J]. Journal of Mountain Science, 2010, 28(5): 579-585. doi: 10.3969/j.issn.1008-2786.2010.05.011
    [13]
    李慧静, 包玉海, 包刚, 等.基于modis-Ndvi的内蒙古植被变化遥感监测[J].测绘科学, 2009, 34(5): 25-27, 51. http://d.old.wanfangdata.com.cn/Thesis/Y1302124

    Li H J, Bao Y H, Bao G, et al. RS monitoring of vegetation change in Inner Mongolia based on MODIS-NDVI[J]. Science of Surveying and Mapping, 2009, 34(5): 25-27, 51. http://d.old.wanfangdata.com.cn/Thesis/Y1302124
    [14]
    汪权方, 李家永.基于时序ndvi数据的鄱阳湖流域常绿覆被季节性变化特征[J].长江流域资源与环境, 2008, 17(6): 866-871. doi: 10.3969/j.issn.1004-8227.2008.06.008

    Wang Q F, Li J Y. Seasonal variation of evergreen land coverage in Poyang Lake watershed using multi-temporal SPOT4-vegetation data[J]. Resources and Environment in the Yangtze Basin, 2008, 17(6): 866-871. doi: 10.3969/j.issn.1004-8227.2008.06.008
    [15]
    章志都, 梁冠巍, 董建文, 等.基于植被分布因子主成分分析的福建省植被区划[J].东北林业大学学报, 2010, 38(3): 61-65. doi: 10.3969/j.issn.1000-5382.2010.03.018

    Zhang Z D, Liang G W, Dong J W, et al. Vegetation regionalization for Fujian Province based on principal component analysis of factors affectiong vegetation distribution[J]. Journal of Northeast Forestry University, 2010, 38(3): 61-65. doi: 10.3969/j.issn.1000-5382.2010.03.018
    [16]
    郭芬芬, 范建容, 汤旭光, 等.基于HJ-1A高光谱数据的藏北高原草地分类方法对比[J].遥感信息, 2013, 28 (1): 77-82, 88. doi: 10.3969/j.issn.1000-3177.2013.01.016

    Guo F F, Fan J R, Tang X G, et al. Comparison of methods for grassland classification based on HJ-1A hyperspectral image data in North Tibet[J]. Remote Sensing Information, 2013, 28(1): 77-82, 88. doi: 10.3969/j.issn.1000-3177.2013.01.016
    [17]
    万华伟, 王昌佐, 李亚, 等.基于高光谱遥感数据的入侵植物监测[J].农业工程学报, 2010, 26(增刊2): 59-63. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ200701003.htm

    Wan H W, Wang C Z, Li Y, et al. Monitoring an invasive plant using hyperspectral remote sensing data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(Suppl.2): 59-63. http://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ200701003.htm
    [18]
    杨可明, 刘飞, 孙阳阳, 等.谐波分析光谱角制图高光谱影像分类[J].中国图象图形学报, 2015, 20(6): 836-844. http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201506014

    Yang K M, Liu F, Sun Y Y, et al. Classification algorithm of hyperspectral imagery by harmonic analysis and spectral angle mapping[J]. Journal of Image and Graphics, 2015, 20(6): 836-844. http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201506014
    [19]
    张莹, 张晓丽, 王书涵, 等.福建将乐林场主要树种冠层光谱反射特征分析[J].西北农林科技大学学报(自然科学版), 2016, 44(2): 83-89, 96. http://d.old.wanfangdata.com.cn/Periodical/xbnydxxb201602012

    Zhang Y, Zhang X L, Wang S H, et al. Spectral reflectance characteristics of canopies of main tree species in Jingle Forest Farm in Fujian[J]. Journal of Northwest A & F University (Natural Science Edition), 2016, 44(2): 83-89, 96. http://d.old.wanfangdata.com.cn/Periodical/xbnydxxb201602012
    [20]
    Congalton R G. A review of assessing the accuracy of classifications of remotely sensed data[J]. Remote Sensing of Environment, 1991, 37(1): 35-46. doi: 10.1016/0034-4257(91)90048-B
  • Related Articles

    [1]Gao Yonglong, Sun Yanli, Xu Mingze, Liu Shan. Variation characteristics in leaf functional traits of woody plants in deciduous broadleaved forest community in Baihua Mountain of Beijing[J]. Journal of Beijing Forestry University, 2024, 46(4): 40-51. DOI: 10.12171/j.1000-1522.20220462
    [2]Xie Shuwen, Jin Guangze, Liu Zhili. Variations and trade-offs of twig-leaf traits for Betula platyphylla with different diameter classes in Xiaoxing’an Mountains of northeastern China[J]. Journal of Beijing Forestry University, 2023, 45(12): 32-40. DOI: 10.12171/j.1000-1522.20210463
    [3]Wang Yongteng, Huang Zhihao, Wang Jun, Zhang Tong, Lang Lihua, Sun Guoming, Cui Guofa. Survival pressure of endangered species Phellodendron amurense[J]. Journal of Beijing Forestry University, 2021, 43(1): 49-57. DOI: 10.12171/j.1000-1522.20200130
    [4]Wei Liping, Han Yanying, Dabuqiong, Gong Wenfeng, Deng Gongfu, Hu Jie. Analysis on phenotypic variation and germplasm resource selection of wild Amygdalus mira in Tibet of southwestern China[J]. Journal of Beijing Forestry University, 2020, 42(7): 48-57. DOI: 10.12171/j.1000-1522.20190422
    [5]Li Jinhang, Zhu Jiyou, Catherine Mhae B. Jandug, Zhao Kai, Xu Chengyang. Relationship between leaf functional trait variation of Cotinus coggygria seedling and location geographical-climatic factors under drought stress[J]. Journal of Beijing Forestry University, 2020, 42(2): 68-78. DOI: 10.12171/j.1000-1522.20190079
    [6]DU Qing-xin, LIU Pan-feng, QING Jun, WEI Yan-xiu, DU Hong-yan.. Variation and probability grading of main quantitative traits of male flowers for Eucommia ulmoides germplasm.[J]. Journal of Beijing Forestry University, 2016, 38(11): 42-49. DOI: 10.13332/j.1000-1522.20160031
    [7]ZHANG Zi-jie, YANG Shan-xun, ZENG Yan-jiang, WANG Rong-gang, WANG Li-ming, PANG Xiao-ming, LI Yue. Variation within clones and families and superior individual selection in different cultivars of Camellia oleifera‘Ruanzhi’.[J]. Journal of Beijing Forestry University, 2016, 38(10): 59-68. DOI: 10.13332/j.1000-1522.20160104
    [8]LIANG De-yang, JIN Yun-zhe, ZHAO Guang-hao, DONG Yuan-hai, LENG Wei-wei, CHEN Chang-lin, WANG Huan, ZHAO Xi-yang. Variance analyses of growth and wood characteristics of 50 Pinus koraiensis clones[J]. Journal of Beijing Forestry University, 2016, 38(6): 51-59. DOI: 10.13332/j.1000-1522.20150465
    [9]ZHANG Zhen, ZHANG Han-guo, ZHOU Yu, LIU Ling, YU Hong-ying, WANG Xu, FENG Wan-ju. Variation of seed characters in Korean pine (Pinus koraiensis ) multi-clonal populations[J]. Journal of Beijing Forestry University, 2015, 37(2): 67-78. DOI: 10.13332/j.cnki.jbfu.2015.02.020
    [10]HUANG Jia-cong, HE Jun, YIN Rui-ping, WAN Xiao-jun, GUO Jun, XIN Cheng-lian, GONG Fa-ping, LI Yue. Variations of fruit and seed traits of natural and artificial populations in Camellia reticulata L[J]. Journal of Beijing Forestry University, 2010, 32(5): 94-101.
  • Cited by

    Periodical cited type(14)

    1. 李一,潘立本,赵雯,穆立蔷,韩文静. 蒙古栎叶片功能性状变化特征及影响因素. 东北林业大学学报. 2024(03): 10-15 .
    2. 白岩松,张雨鉴,秦倩倩,孙兴悦,刘艳红. 大兴安岭典型灌木叶片功能性状对环境因子的响应. 生态学杂志. 2024(01): 131-139 .
    3. 郭文芳,陈艳梅,高飞,王佳乐. 太行山7种药用植物性状特征及其对土壤因子的响应. 环境工程技术学报. 2024(02): 612-621 .
    4. 欧芷阳,郑威,庞世龙,何峰,申文辉,谭一波,陈始贵. 广西猫儿山优势木本植物叶功能性状关联性沿海拔梯度的变化规律. 生态科学. 2024(02): 95-101 .
    5. 周会萍,姜金矜,黄艳丽,庄静静. 两种生活型木本植物叶功能性状变异特征研究. 沈阳农业大学学报. 2024(03): 298-305 .
    6. 冯浩育,陈思帆,索奥丽,龚俊伟,陈锋,刘晓东. 不同火烈度下山西太岳山油松林灌木层物种多样性和叶功能性状. 北京林业大学学报. 2024(06): 38-47 . 本站查看
    7. 谢立红,黄庆阳,曹宏杰,杨帆,王继丰,杨立宾. 五大连池火山黑桦叶性状对生境因子的响应. 中南林业科技大学学报. 2024(05): 112-124 .
    8. 谢立红,黄庆阳,曹宏杰,王继丰,王建波,倪红伟. 五大连池火山蒙古栎种群空间分布格局. 生态与农村环境学报. 2023(07): 896-906 .
    9. 黄庆阳,谢立红,曹宏杰,王立民,杨帆,王继丰,刘赢男,倪红伟. 细菌对五大连池火山森林凋落物早期分解的影响. 应用生态学报. 2023(07): 1941-1948 .
    10. 刘爱林,张往祥,刘陈妤,高亮,杨晓倩,周婷,崔珺. 7种樟科植物叶功能性状及其对土壤因子的响应. 福建农业学报. 2023(12): 1428-1436 .
    11. 杨巧,朱润军,杨畅宇,李仕杰,程希平. 基于树形结构的木棉叶功能性状差异性研究. 生态学报. 2022(07): 2834-2842 .
    12. 王佳欢,刘彦林,曹鹤,杨新兵,赵迎雪,陈一凡,史宝胜. 采石场高陡岩壁坡向对葛藤叶形态及光合特征的影响. 东北林业大学学报. 2022(05): 47-51+75 .
    13. 顾泽,王博,陈思帆,王忆文,索奥丽,刘晓东,陈锋. 不同火烈度火烧迹地内油松叶功能性状的变化. 应用生态学报. 2022(06): 1497-1504 .
    14. 谢立红,黄庆阳,曹宏杰,杨帆,王继丰,王建波,倪红伟. 五大连池火山蒙古栎种群结构及动态特征. 浙江农林大学学报. 2022(05): 960-970 .

    Other cited types(6)

Catalog

    Article views (2849) PDF downloads (138) Cited by(20)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return