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

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Remote sensing Remote sensing Multispectral photography Image processing.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

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.

Full text
Abstract:
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 recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of <i>deep learning</i> techniques, such as <i>convolutional neural networks</i> (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, <i>end-to-end learning</i> is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. <br><br> We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of <i>deep learning</i> techniques, such as <i>convolutional neural networks</i> (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, <i>end-to-end learning</i> is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. <br><br> We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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. Main stages of the development of cartographic models have been described: initial processing of the aerial images and their visualization, preliminary pre-field interpretation (classification) of the images on the basis of unsupervised automated classification, field studies (geobotanical records and GPS measurements at the sites chosen at previous stage). Post-field stage had the following sub-stages: final geometric correction of the digital images, elaboration of the classification system for the main mapping subdivisions, final supervised automated classification on the basis of expert assessment. By systematizing clusters of the obtained classified image the cartographic models of the study area have been made. Application of the new technology of remote sensing allowed making qualitative and quantitative assessment of modern state of rangelands.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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 resolution but lower spectral resolution than the other bands. In the field of remote sensing, there is a long tradition of applying pan-sharpening to satellite images, i.e. bringing the multispectral bands to the higher spatial resolution by merging them with the panchromatic band. To our knowledge there are so far no approaches to super-resolution which take advantage of the panchromatic band. In this paper we propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles in order to create super-resolved analytic images. The derived quality metrics show that the method improves information content of the processed images. We compare the results created by four CNN architectures, with RedNet30 performing best.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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 higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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 in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements.
 Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics
 
 References 
 Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32.
 Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474.
 Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728.
 Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603.
 Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153.
 Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191.
 Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press.
 Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54.
 Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27.
 Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209.
 Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249.
 Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132.
 Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press.
 Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press.
 Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer.
 Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651.
 Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309.
 Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194.
 Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401.
 Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206.
 Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272.
 Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1)
 Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676.
 Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328.
 Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429.
 Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on.
 Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52.
 Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents.
 Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/
 Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337.
 Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734.
 Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261.
 Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016.
 Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691.
 Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124.
 Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82.
 Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406.
 Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439.
 Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106.
 Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120.
 Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600.
 Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55.
 Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70.
 Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380.
 Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670.
 Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234.
 Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer.
 Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411.
 Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304.
 Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons.
 Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088.
 López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285.
 Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720.
 Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188.
 Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250.
 Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261.
 Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357.
 Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769.
 Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179.
 Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137.
 Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206.
 Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4)
 Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337.
 Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer.
 Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161.
 Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133.
 Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26.
 Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556.
 Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269.
 Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854.
 Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88.
 Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262.
 Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84.
 Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234.
 Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611.
 Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16.
 Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA.
 Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55.
 Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136.
 Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13.
 Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469.
 Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer.
 Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36.
 Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93.
 Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285.
 Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer.
 Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668.
 Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579.
 Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183.
 Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business.
 Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203.
 Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893.
 Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress.
 Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323.
 Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer.
 Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811.
 Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281.
 Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.
 
 
 
 
 
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
<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>High-detail remote sensing from UAV’s is a key level of monitoring of Northern forests at a large-scale level, ensuring the correct transition from sub - satellite ground-based studies to thematic products obtained from multi-time Hyper-and multispectral data of medium and relatively high resolution (MODIS, LANDSAT, Sentinel-2).</p><p>When planning and conducting specific case studies based on UAV data, special attention should be paid to the justification of the survey methodology. In particular, the choice of a strictly defined high-altitude echelon of the survey determines the recognition of the objects of study and the possibility of reliable determination of its properties and features. To study the parameters of forest ecosystems at the level of individual trees and at the level of forest plantations, we selected two different-height echelons of survey from ultra-low altitudes: from 50 m, which allowed us to obtain ultra-high-detailed data for each sample area provided by detailed ground-based studies with sub-tree account, and from 100 m-to obtain derived characteristics of forest communities within the area equivalent to 3 pixels of thematic MODIS products with a spatial resolution of 250 m. The data of optical survey with UAV were obtained in July 2018 for 22 plots located in the central part of the Kola Peninsula and representative of different types of North taiga stands and their dynamics under climate change.</p><p>At the stage of preprocessing images were obtained dense point clouds, characterizing both vertical and horizontal structure of stands. Digital terrain and terrain models and tree canopy models were obtained after cloud filtering and classification. Algorithms of automated segmentation and classification have been developed and tested to obtain such characteristics of stands as the height of individual trees, the area of crown projections, the projective cover of the tree-shrub layer. The obtained characteristics are aggregated by cells of a regular network with the dimension corresponding to the spatial resolution of Sentinel-2 and Landsat-8 data.</p><p>The main results of the works are digital spatial datasets for 22 sample plots: raw data with very high resolution imagery (optical images with very high resolution, dense point clouds, RGB-orthophoto) and create based on a thematic derivative products (digital terrain model, topography, tree canopy cover; map of the heights and projections of the crowns of trees, percent cover of tree and shrub vegetation).</p>
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 spectral feature based classifiers and found to be consistently better. Simulation study revealed that Biorthogonal 3.3 (Bior3.3) wavelet in combination with MLP performed better compared to all other wavelets. Results are evaluated visually and quantitatively with two measurements, β index of homogeneity and Davies–Bouldin (DB) index for compactness and separability of classes. We suggested a modified β index in accessing the percentage of accuracy (PAβ) of the classified images also.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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 discussed.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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 detecting hydrothermal alteration and for uranium exploration. Although these papers indicate the importance of physical properties and models in the interpretation of remote sensing data, the studies were constrained by the instruments that collected the data and by the availability of image‐processing software. Circumstances have changed significantly in the intervening decade, as illustrated in recent review papers (Watson, 1985; Goetz et al., 1983) and demonstrated by the papers in this Special Issue.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Remote sensing Remote sensing Multispectral photography Image processing"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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/.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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).
APA, Harvard, Vancouver, ISO, and other styles
9

Doster, Timothy J. "Mathematical methods for anomaly grouping in hyperspectral images /." Online version of thesis, 2009. http://hdl.handle.net/1850/11575.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

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

1

Hyperspectral remote sensing. SPIE, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

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

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

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

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!