Relevant bibliographies by topics / Remote sensing Remote sensing Multispectral photography Image processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Remote sensing Remote sensing Multispectral photography Image processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

1

Liebel, L., and M. Körner. "SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 883–90. http://dx.doi.org/10.5194/isprs-archives-xli-b3-883-2016.

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In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for <i>single-image super resolution</i> are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that rec
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Liebel, L., and M. Körner. "SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 883–90. http://dx.doi.org/10.5194/isprsarchives-xli-b3-883-2016.

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In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for <i>single-image super resolution</i> are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper,
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Rachkovskaya, E. I., S. S. Temirbekov, and R. E. Sadvokasov. "Application of remote sensing methods for assessment of anthropogenic transformation of rangelands." Geobotanical mapping, no. 1998-2000 (2000): 16–25. http://dx.doi.org/10.31111/geobotmap/1998-2000.16.

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Capabilities of the remote sensing methods for making maps of actual and potential vegetation, and assessment of the extent of anthropogenic transformation of rangelands are presented in the paper. Study area is a large intermountain depression, which is under intensive agricultural use. Color photographs have been made by Aircraft camera Wild Heerburg RC-30 and multispectral scanner Daedalus (AMS) digital aerial data (6 bands, 3.5m resolution) have been used for analysis of distribution and assessment of the state of vegetation. Digital data were processed using specialized program ENVI 3.0.
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Müller, M. U., N. Ekhtiari, R. M. Almeida, and C. Rieke. "SUPER-RESOLUTION OF MULTISPECTRAL SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 33–40. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-33-2020.

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Abstract. Super-resolution aims at increasing image resolution by algorithmic means and has progressed over the recent years due to advances in the fields of computer vision and deep learning. Convolutional Neural Networks based on a variety of architectures have been applied to the problem, e.g. autoencoders and residual networks. While most research focuses on the processing of photographs consisting only of RGB color channels, little work can be found concentrating on multi-band, analytic satellite imagery. Satellite images often include a panchromatic band, which has higher spatial resolut
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Siok, Katarzyna, Ireneusz Ewiak, and Agnieszka Jenerowicz. "Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images." Sensors 20, no. 24 (2020): 7100. http://dx.doi.org/10.3390/s20247100.

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The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a hig
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Yakubu, Bashir Ishaku, Shua’ib Musa Hassan, and Sallau Osisiemo Asiribo. "AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES." Geosfera Indonesia 3, no. 2 (2018): 27. http://dx.doi.org/10.19184/geosi.v3i2.7934.

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Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm
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Medvedev, Andrey, Arseny Kudikov, Natalia Telnova, Olga Tutubalina, Elena Golubeva, and Mikhail Zimin. "Multiscale assessment of northern forest characteristics based on ultra-high resolution data." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-246-2019.

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<p><strong>Abstract.</strong> The algorithms for quantitative estimates of various structural and functional parameters of forest ecosystems, particularly boreal forests, on high resolution remote sensing data are actively developing since the mid-2000s. For monitoring of forest ecosystems located at the Northern limit of distribution, effective not only lidar data but also the optical data obtained by unmanned aerial vehicles (UAV’s) with ultra-low altitude photography and derived products resulting from modern algorithms for the photogrammetric processing.</p><p&gt
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SHANKAR, B. UMA, SAROJ K. MEHER, and ASHISH GHOSH. "NEURO-WAVELET CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (2007): 589–611. http://dx.doi.org/10.1142/s0219691307001914.

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A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels information using wavelet transform (WT) are fed as input to a feed forward multi-layer neural network (MLP). The WT basically provides the spatial and spectral features of a pixel along with its neighbors and these features are used for improved classification. For testing the performance of the proposed method, we have used two IRS-1A satellite images and one SPOT satellite image. Results are compared with those of the original sp
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Zhao, Yu, Fan Feng Meng, and Jiang Feng. "Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods." Advanced Materials Research 905 (April 2014): 585–88. http://dx.doi.org/10.4028/www.scientific.net/amr.905.585.

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In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discus
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Watson, Kenneth. "Introduction to the Special Issue on remote sensing." GEOPHYSICS 52, no. 7 (1987): 839–40. http://dx.doi.org/10.1190/1.1442355.

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In 1977, the first Special Issue on remote sensing published by Geophysics contained papers selected from two special sessions at the 45th Annual International SEG Meeting, October 12–16, 1975, in Denver, Colorado. That first Special Issue consisted of eight papers: four are primarily tutorial (image processing, spectral signatures in the visible and near infrared, microwave spectra of layered media, and factor analysis of gamma‐ray spectrometry), two involve structural interpretations with implications for mineral exploration and seismicity, and two examine multispectral reflectance data for
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Dissertations / Theses on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

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Munechika, Curtis K. "Merging panchromatic and multispectral images for enhanced image analysis /." Online version of thesis, 1990. http://hdl.handle.net/1850/11366.

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Lewis, Ryan H. "Topological & network theoretic approaches in hyperspectral remote sensing /." Online version of thesis, 2010. http://ritdml.rit.edu/handle/1850/12274.

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Francis, John W. "Pixel-by pixel reduction of atmospheric haze effects in multispectral digital imagery of water /." Online version of thesis, 1989. http://hdl.handle.net/1850/11359.

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Pradhan, Pushkar S. "Multiresolution based, multisensor, multispectral image fusion." Diss., Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/etd/show.asp?etd=etd-07082005-140541.

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Daniel, Brian. "A system study of sparse aperture sensors in remote sensing applications with explicit phase retrieval /." Online version of thesis, 2009. http://hdl.handle.net/1850/9676.

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Higbee, Shawn. "A Bayesian approach to identification of gaseous effluents in passive LWIR imagery /." Online version of thesis, 2009. http://hdl.handle.net/1850/10969.

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Aqdus, Syed Ali. "Airborne multispectral and hyperspectral remote sensing techniques in archaeology a comparative study /." Thesis, Thesis restricted. Connect to e-thesis to view abstract, 2009. http://theses.gla.ac.uk/812/.

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Thesis (Ph.D.) - University of Glasgow, 2009.<br>Ph.D. thesis submitted to the Faculty of Physical Sciences, Department of Geographical and Earth Sciences and the Faculty of Arts, Department of Archaeology, University of Glasgow, 2009. Includes bibliographical references. Print version also available.
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Whitbread, P. J. "Multi-spectral texture : improving classification of multi-spectral images by the integration of spatial information /." Title page, abstract and contents only, 1992. http://web4.library.adelaide.edu.au/theses/09PH/09phw5792.pdf.

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Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994?<br>One computer disk in pocket inside back cover. System requirements for accompanying computer disk: Macintosh computer. Includes bibliographical references (leaves 148-160).
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Doster, Timothy J. "Mathematical methods for anomaly grouping in hyperspectral images /." Online version of thesis, 2009. http://hdl.handle.net/1850/11575.

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Jayaram, Vikram. "Reduced dimensionality hyperspectral classification using finite mixture models." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Books on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

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Hyperspectral remote sensing. SPIE, 2012.

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Henri, Maître, State Key Laboratory for Multi-spectral Information Processing Technologies., SPIE (Society), and SPIE (Society), eds. MIPPR 2007: Multispectral image processing : 15-17 November 2007, Wuhan, China. SPIE, 2007.

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International Symposium on Multispectral Image Processing and Pattern Recognition (4th 2005 Wuhan, China). SAR and multispectral image processing: 31 October-2 November 2005, Wuhan, China. Edited by Zhang Liangpei, Zhang Jianqing, Liao Mingsheng 1962-, Ce hui yao gan xin xi gong cheng guo jia zhong dian shi yan shi., Wuhan da xue, and Society of Photo-optical Instrumentation Engineers. SPIE, 2005.

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International Symposium on Multispectral Image Processing and Pattern Recognition (4th 2005 Wuhan, China). Image analysis techniques : 31 October-2 November 2005, Wuhan, China. Edited by Li Deren, Ma Hongchao, Ce hui yao gan xin xi gong cheng guo jia zhong dian shi yan shi., Wuhan da xue, and Society of Photo-optical Instrumentation Engineers. SPIE, 2005.

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International Symposium on Multispectral Image Processing and Pattern Recognition (6th 2009 Yichang Shi, China). MIPPR 2009: Multispectral image acquisition and processing : 30 October-1 November 2009, Yichang, China. Edited by Udupa Jayaram K, Hua zhong gong xue yuan, National Laboratory for Multi-spectral Information Processing Technologies, and SPIE (Society). SPIE, 2009.

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K, Udupa Jayaram, Hua zhong gong xue yuan, National Laboratory for Multi-spectral Information Processing Technologies, and SPIE (Society), eds. MIPPR 2009: Multispectral image acquisition and processing : 30 October-1 November 2009, Yichang, China. SPIE, 2009.

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National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Guilin dian zi ke ji da xue, Hua zhong gong xue yuan, SPIE (Society), and International Symposium on Multispectral Image Processing and Pattern Recognition (7th : 2011 : Guilin, China), eds. MIPPR 2011: Remote sensing, image processing, geographic information systems, and other applications. SPIE, 2011.

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Borengasser, Marcus. Hyperspectral remote sensing: Principles and applications. CRC Press, 2008.

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Hanqing, Lu, Zhang T, Zhongguo ke xue yuan. Institute of Automation. National Laboratory of Pattern Recognition., et al., eds. Multispectral image processing and pattern recognition: Third International Symposium on Multispectral Image Processing and Pattern Recognition : 20-22 October, 2003, Beijing, China. SPIE, 2003.

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1963-, Arora M. K., ed. Advanced image processing techniques for remotely sensed hyperspectral data. Springer, 2004.

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Book chapters on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

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Gupta, Ravi Prakash. "Digital Image Processing of Multispectral Data." In Remote Sensing Geology. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05283-9_10.

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Gupta, Ravi P. "Digital Image Processing of Multispectral Data." In Remote Sensing Geology. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-55876-8_13.

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Tintrup, Frank, Cristina Perra, and Gianni Vernazza. "Classification of Compressed Multispectral Data." In Machine Vision and Advanced Image Processing in Remote Sensing. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60105-7_24.

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Ose, Kenji, Thomas Corpetti, and Laurent Demagistri. "Multispectral Satellite Image Processing." In Optical Remote Sensing of Land Surface. Elsevier, 2016. http://dx.doi.org/10.1016/b978-1-78548-102-4.50002-8.

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SOLAIMAN, B. "INFORMATION FUSION FOR MULTISPECTRAL IMAGE CLASSIFICATION POST-PROCESSING." In Information Processing For Remote Sensing. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789812815705_0016.

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LANDGREBE, DAVID. "INFORMATION EXTRACTION PRINCIPLES AND METHODS FOR MULTISPECTRAL AND HYPERSPECTRAL IMAGE DATA." In Information Processing For Remote Sensing. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789812815705_0001.

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Conference papers on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

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Tong, Qingxi, Lanfen Zheng, Yongqi Xue, Bing Zhang, Yongchao Zhao, and Liangyun Liu. "Hyperspectral remote sensing in China." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441358.

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Li, Deren, and Liangpei Zhang. "Processing of hyperspectral remote sensing images." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323645.

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Liu, Zhaohua, and Jingyu Yang. "Remote sensing image parallel processing system." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Jianguo Liu, Kunio Doi, Aaron Fenster, and S. C. Chan. SPIE, 2009. http://dx.doi.org/10.1117/12.832392.

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Su, Lihong, Yuxia Huang, and Xiaowen Li. "Three-dimensional landscape modeling for remote sensing." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441611.

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Xu, Sheng, Huo Hong, Tao Fang, and Deren Li. "Shape saliency for remote sensing image." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Yongji Wang, Jun Li, Bangjun Lei, and Jingyu Yang. SPIE, 2007. http://dx.doi.org/10.1117/12.751123.

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Ni, Lin. "Fast fractal coding of multispectral remote sensing images." In Multispectral Image Processing and Pattern Recognition, edited by Jun Tian, Tieniu Tan, and Liangpei Zhang. SPIE, 2001. http://dx.doi.org/10.1117/12.442904.

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Cai, Jia, Jing-xuan Ma, and Jian-xia Wang. "Multispectral image and fullcolor remote sensing image processing technology." In 2017 9th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2017. http://dx.doi.org/10.1109/icmic.2017.8321705.

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Huo, Chunlei, Keming Chen, Zhixin Zhou, and Hanqing Lu. "Hybrid approach for remote sensing image registration." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Yongji Wang, Jun Li, Bangjun Lei, and Jingyu Yang. SPIE, 2007. http://dx.doi.org/10.1117/12.749592.

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Gui, Yufeng, Xinping Xiao, Jixian Zhang, Zongjian Lin, and Yuanying Mou. "Remote sensing image change detection using Gray system theory." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441374.

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Liu, Liangming, and Deren Li. "Drought analysis based on remote sensing and ancillary data." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441382.

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