Modification of Spectral Rule-based Classifier based on multispectral remote sensing images and its application in islands and coastal zones

  • DING Ling ,
  • CHEN Jianyu ,
  • ZHU Qiankun ,
  • CHEN Ninghua
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  • 1. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200230, China;
    2. Second Institute of Oceanography, MNR, Hangzhou 310012, China;
    3. School of Earth Sciences, Zhejiang University, Hangzhou 310058, China

Received date: 2022-01-10

  Online published: 2023-02-09

Abstract

Based on the unsupervised pixel-based Spectral Rule-based Classifier (SRC) algorithm, an effective Modified Spectral Rule-based Classifier (MSRC) was proposed considering the influence of atmospheric correction on spectral reflectances of remote sensing images. MSRC modifies rule sets according to ground object spectrum curves and spectral indices, optimizes spectral categories through refined and supplementary rules as well as modified thresholds. The Landsat 8 remote sensing images of islands (Jiapeng, Qi'ao) and coastal zones (Quanwan, Huidong) in the Pearl River Delta were chosen as the experimental data. The band reflectances and ground object spectrum curves before and after atmospheric correction process were contrasted. Classification results and accuracy of the MSRC algorithm were analyzed and compared with those of other six classification algorithms: the original SRC algorithm, Minimum Distance Classification (MDC) algorithm, Maximum Likelihood Classification (MLC) algorithm, Support Vector Machine (SVM) algorithm, Neural Network Classification (NNC) algorithm and spectral indices-based classification methods. The overall accuracy (OA) of MSRC algorithm using experimental data were respectively 87.66%, 82.38%, 77.67% and 80.05%, which were all higher than those of the original SRC algorithm, MDC, MLC and spectral indices-based classification methods, and closed to the accuracy of supervised algorithms (SVM and NNC) without the requirement of manually labelling the training dataset. MSRC performs well in land-cover type scenarios of islands and coastal zones on Landsat 8 multispectral remote sensing images.

Cite this article

DING Ling , CHEN Jianyu , ZHU Qiankun , CHEN Ninghua . Modification of Spectral Rule-based Classifier based on multispectral remote sensing images and its application in islands and coastal zones[J]. Journal of Marine Sciences, 2022 , 40(4) : 38 -51 . DOI: 10.3969j.issn.1001-909X.2022.04.004

References

[1] 李清泉,卢艺,胡水波,等.海岸带地理环境遥感监测综述[J].遥感学报,2016,20(5):1216-1229.
LI Qingquan, LU Yi, HU Shuibo, et al. Review of remotely sensed geo-environmental monitoring of coastal zones[J]. Journal of Remote Sensing, 2016, 20(5): 1216-1229.
[2] 林珲,张鸿生.热带与亚热带遥感:需求、挑战与机遇[J].遥感学报,2021,25(1):276-291.
LIN Hui, ZHANG Hongsheng. Tropical and subtropical remote sensing: Needs, challenges, and opportunities[J]. National Remote Sensing Bulletin, 2021, 25(1): 276-291.
[3] 宫鹏,张伟,俞乐,等.全球地表覆盖制图研究新范式[J].遥感学报,2016,20(5):1002-1016.
GONG Peng, ZHANG Wei, YU Le, et al. New research paradigm for global land cover mapping[J]. Journal of Remote Sensing, 2016, 20(5): 1002-1016.
[4] CHEN Jianyu, PAN Delu, MAO Zhihua, et al. Land-cover reconstruction and change analysis using multisource remotely sensed imageries in Zhoushan Islands since 1970[J]. Journal of Coastal Research, 2014, 30(2): 272-282.
[5] PHIRI D, MORGENROTH J. Developments in Landsat land cover classification methods: A review[J]. Remote Sensing, 2017, 9(9): 967.
[6] BLASCHKE T, HAY G J, KELLY M, et al. Geographic object-based image analysis-Towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 180-191.
[7] BARALDI A, PUZZOLO V, BLONDA P, et al. Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM+ images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2563-2586.
[8] BARALDI A, DURIEUX L, SIMONETTI D, et al. Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 imagery—Part I: system design and implemen-tation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3): 1299-1325.
[9] BARALDI A, BOSCHETTI L, HUMBER M L. Probability sampling protocol for thematic and spatial quality assessment of classification maps generated from spaceborne/airborne very high resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 701-760.
[10] BOSCHETTI L, ROY D P, JUSTICE C O, et al. MODIS-Landsat fusion for large area 30 m burned area mapping[J]. Remote Sensing of Environment, 2015, 161: 27-42.
[11] ARVOR D, BETBEDER J, DAHER F R G, et al. Towards user-adaptive remote sensing: Knowledge-driven automatic classification of Sentinel-2 time series[J]. Remote Sensing of Environment, 2021, 264: 112615.
[12] ANDRÉS S, ARVOR D, MOUGENOT I, et al. Ontology-based classification of remote sensing images using spectral rules[J]. Computers & Geosciences, 2017, 102: 158-166.
[13] TIEDE D, BARALDI A, SUDMANNS M, et al. Archi-tecture and prototypical implementation of a semantic querying system for big Earth observation image bases[J]. European Journal of Remote Sensing, 2017, 50(1): 452-463.
[14] LANG S, HAY G, BARALDI A, et al. GEOBIA achie-vements and spatial opportunities in the era of big earth observation data[J]. ISPRS International Journal of Geo-Information, 2019, 8(11): 474.
[15] 徐春燕,冯学智.TM图像大气校正及其对地物光谱响应特征的影响分析[J].南京大学学报:自然科学版,2007,43(3):309-317.
XU Chunyan, FENG Xuezhi. Atmospheric correction on TM image and its influence analysis on spectral response characteristics[J]. Journal of Nanjing University: Natural Sciences, 2007, 43(3): 309-317.
[16] MORAVEC D, KOMÁREK J, MEDINA S L C, et al. Effect of atmospheric corrections on NDVI: Intercompa-rability of Landsat 8, Sentinel-2, and UAV Sensors[J]. Remote Sensing, 2021, 13(18): 3550.
[17] 徐涵秋.从增强型水体指数分析遥感水体指数的创建[J].地球信息科学,2008,10(6):776-780.
XU Hanqiu. Comment on the enhanced water index(EWI): A discussion on the creation of a water index[J]. Geo-Information Science, 2008, 10(6): 776-780.
[18] 曹勇,陶于祥,邓陆,等.一种抑制裸地的不透水面指数构建[J].国土资源遥感,2020,32(3):71-79.
CAO Yong, TAO Yuxiang, DENG Lu, et al. An impervious surface index construction for restraining bare land[J]. Remote Sensing for Land & Resources, 2020, 32(3): 71-79.
[19] 侯西勇,邸向红,侯婉,等.中国海岸带土地利用遥感制图及精度评价[J].地球信息科学学报,2018,20(10):1478-1488.
HOU Xiyong, DI Xianghong, HOU Wan, et al. Accuracy evaluation of land use mapping using remote sensing techniques in coastal zone of China[J]. Journal of Geo-Information Science, 2018, 20(10): 1478-1488.
[20] TALUKDAR S, SINGHA P, MAHATO S, et al. Land-use land-cover classification by machine learning classifiers for satellite observations—a review[J]. Remote Sensing, 2020, 12(7): 1135.
[21] VERMOTE E F, TANRE D, DEUZE J L, et al. Second simulation of the satellite signal in the solar spectrum, 6S: An overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675-686.
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