Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Analysis and Discussion
Conclusion
References
All Topics
Computer Science
Computer Vision

Image Fusion Techniques in Remote Sensing

Profile image of Reham GharbiaReham Gharbia

Abstract

Remote sensing image fusion is an effective way to use a large volume of data from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS and QuickBird provide both panchromatic (Pan) images at a higher spatial resolution and multispectral (MS) images at a lower spatial resolution and many remote sensing applications require both high spatial and high spectral resolutions, especially for GIS based applications. An effective image fusion technique can produce such remotely sensed images. Image fusion is the combination of two or more different images to form a new image by using a certain algorithm to obtain more and better information about an object or a study area than. The image fusion is performed at three different processing levels which are pixel level, feature level and decision level according to the stage at which the fusion takes place. There are many image fusion methods that can be used to produce high resolution multispectral images from a high resolution pan image and low resolution multispectral images. This paper explores the major remote sensing data fusion techniques at pixel level and reviews the concept, principals, limitations and advantages for each technique. This paper focused on traditional techniques like intensity hue-saturation-(HIS), Brovey, principal component analysis (PCA) and Wavelet.

Figures (4)
information within all the inputs can be compressed into a much smaller amount of outputs without much loss of information [34].
information within all the inputs can be compressed into a much smaller amount of outputs without much loss of information [34].
Fig.3 scheme of Wavelet image fusion appropriately, improving the results of the classical methods. This has been verified with smooth images and also with medical images, where no significant changes are present.
Fig.3 scheme of Wavelet image fusion appropriately, improving the results of the classical methods. This has been verified with smooth images and also with medical images, where no significant changes are present.

Related papers

COMPARISON AND ANALYSIS OF REMOTE SENSING DATA FUSION TECHNIQUES AT FEATURE AND DECISION LEVELS
John van Genderen

Image fusion is the combination of two or more different images to form a new image by using a certain algorithm. The aim of image fusion is to integrate complementary data in order to obtain more and better information about an object or a study area than can be derived from single sensor data alone. Image fusion can be performed at three different processing levels which are pixel level, feature-level and decision-level according to the stage at which the fusion takes place. This paper explores the major remote sensing data fusion techniques at feature and decision levels implemented as found in the literature. It compares and analyses the process model and characteristics including advantages, limitations and applicability of each technique, and also introduces some practical applications. It concludes with a summary and recommendations for selection of suitable methods.

View PDFchevron_right
A new wavelet-based image fusion method for remotely sensed data
Peijun Shi

Int J Remote Sens, 2006

The preservation of spectral characteristics and the retention of high spatial resolution are two key issues in image fusion. Different applications may require different balances between the two. However, the limitation is that only one fused result is possible for most of the commonly used fusion methods; users have no control over how much spatial detail or spectral information should be retained. Both quantitative and qualitative criteria, including mean, standard deviation, entropy, average gradient, spectral distortion, correlation coefficient and visual assessment, were used to evaluate the performance of the proposed fuser. A SPOT (Satellite Pour l'Observation de la Terre) panchromatic image and Landsat Thematic Mapper (TM) images were employed in the evaluation. The results show that the proposed method is slightly better than, or comparable to, commonly used image fusion methods, including Intensity-Hue-Saturation (IHS), principal component analysis (PCA), and two other wavelet-based fusion methods with replacement and with selection strategies, in terms of spectral characteristic preservation and high spatial resolution retention. Moreover, the proposed method can achieve a wide range of balances between spectral characteristic preservation and high spatial resolution retention, and thus is applicable in different applications. low spatial resolution images using the IHS system and then substituting the *Corresponding author.

View PDFchevron_right
Image Fusion for Remote Sensing Applications
Leila Fonseca

Image Fusion and Its Applications, 2011

View PDFchevron_right
Digital Image Fusion Techniques: A Review
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

Image Fusion is a technique of obtaining images with high spatial and spectral resolution from low spatial resolution multispectral and high spatial resolution panchromatic images. There is often an inverse relationship between the spectral and spatial resolution of the image. It has not been possible to propose a single sensor package that will meet all our application requirements, while the combined image from multiple sensors will provide more comprehensive information by collecting a wide diversity of sensed wavelengths and spatial resolutions. Due to the demand for higher classification accuracy and the need in enhanced positioning precision there is always a need to improve the spectral and spatial resolution of remotely sensed imagery. These requirements can be fulfilled by the utilization of image processing techniques at a significantly lower expense. The goal is to combine image data to form a new image that contains more interpretable information than can be gained by using the original information. Ideally the fused data should not distort the spectral characteristics of multispectral data as well as it should retain the basic colour content of the original data. If the fused images are used for classification, then the commonly used merging methods are Principal Component Analysis(PCA), Intensity hue saturation method (IHS), Brovey transformation, multiplicative technique (MT), High-pass Filter (HPF), Smoothing Filter-based Intensity Modulation(SFIM) and Wavelet Transform.

View PDFchevron_right
Tools and methods for fusion of images of different spatial resolution
Christine Pohl

The usefulness of remote sensing data, in particular of images from Earth observation satellites, largely depends on their spectral, spatial and temporal resolution. Each system has its specific characteristics providing different types of parameters on the observed objects. Focussing on operational and most commonly used commercial remote sensing satellite sensors, this paper describes how image fusion techniques can help increase the usefulness of the data acquired. There are plenty of possibilities of combining images from different satellite sensors. This paper concentrates on the existing techniques that preserve spectral characteristics, while increasing the spatial resolution. A very common example is the fusion of SPOT XS with PAN data to produce multispectral (3band) imagery with 10 m ground resolution. These techniques are also referred to as image sharpening techniques. A distinction has to be made between the pure visual enhancement (superimposition) and real interpolation of data to achieve higher resolution (e.g. wavelets). In total, the paper describes a number of fusion techniques, such as RGB colour composites, Intensity Hue Saturation (IHS) transformation, arithmetic combinations (e.g. Brovey transform), Principal Component Analysis, wavelets (e.g. ARSIS method) and Regression Variable Substitution (RVS), in terms of concepts, algorithms, processing, achievements and applications. It is mentioned in which way the results of various techniques are influenced by using different pre-processing steps as well as modifications of the involved parameters. All techniques are discussed and illustrated using examples of applications in the various fields that are part of ITC's educational programme and consulting projects.

View PDFchevron_right
Review article Multisensor image fusion in remote sensing: concepts, methods and applications
John Van Genderen-2

International journal of remote sensing, 1998

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research ® eld at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

View PDFchevron_right
Multisensor image fusion in remote sensing: concepts, methods and applications
Ijaz Ahmed

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research ® eld at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

View PDFchevron_right
9 Image Fusion for Remote Sensing Applications
KRIMO AKBI
View PDFchevron_right
Multisensor image fusion techniques in remote sensing
Manfred Ehlers

ISPRS Journal of Photogrammetry and Remote Sensing, 1991

University of Maine Orono, ME 04469, USA Commission VII Current and future remote sensing programs such as Landsat, SPOT, MOS, ERS, JERS, and the space platform's Earth Observing System (Eos) are based on a variety of imaging sensors that will provide timely and repetitive multisensor earth observation data on a global scale. Visible, infrared and microwave images of high spatial and spectral resolution will eventually be available for all parts of the earth. It is essential that efficient processing techniques be developed to cope with the large multisensor data volumes. This paper discusses data fusion techniques that have proved successful for synergistic merging of SPOT HRV, Landsat TM and SIR-B

View PDFchevron_right
A Survey on Image Fusion Techniques
Ijesrt Journal

International Journal of Engineering Sciences & Research Technology, 2014

Image fusion techniques have interest within the remote sensing community. The reason of this is that in most cases the new generation of remote sensors with very high spatial resolution acquires image datasets in two separate modes: the highest spatial resolution is obtained for panchromatic images (PAN) whereas multispectral information (MS) is associated with lower spatial resolution. Usually, the term ‘fusion’ gets several words to appear, such as merging, combination, synergy, integration and several others that express more or less the same meaning the concept have since it appeared in literature. Image fusion techniques can be classified into three categories depending on the stage at which fusion takes place; it is often divided into three levels, namely: pixel level, feature level and decision level of representation. This paper describes the concept of image fusion and its relevant methods.

View PDFchevron_right

References (56)

  1. L. Bruzzone, F. Melgani, A data fusion approach to unsupervised change detection, in: Geoscience and Remote Sensing Symposium, IGARSS 03.Proceedings, IEEE International, 2003, vol. 2, Toulouse, Pp. 1374- 1376.
  2. P. Gamba, J. Chanussot, Guest editorial foreword to the special issue on data fusion, IEEE Trans. Geosci. Remote Sens. 46, 2008, Pp. 1283-1288.
  3. C. Pohl, J.L. Van Genderen, Multisensor image fusion in remote sensing: concepts, methods, and applications, International Journal of Remote Sensing 19 (5), 1998, Pp. 823-854.
  4. D. Amarsaikhan, T. Douglas, Data fusion and multisource image classification, International. Journal of Remote Sensing 25, 2004, Pp. 3529-3539.
  5. L. Bruzzone, D. Fernandez-Prieto, S. B. Serpico, A neural-statistical approach to multitemporal and multisource remote sensing image classification, IEEE Trans. Geosci. Remote Sensing. 37, 1999, Pp. 1292-1305.
  6. M. Fauvel, J. Chanussot, J.A. Benediktsson, Decision fusion for the classification of urban remote sensing images, IEEE Trans. Geosci. Remote Sens. 44, 2006, Pp.2828-2838.
  7. B. Jeon, Decision fusion approach for multitemporal classification, IEEE Trans. Geosci. Remote Sens. 37, 1999, Pp. 1227-1233.
  8. A. Goshtasby and S. Nikolov, Image fusion: Advances in the state of the art, Guest editorial /Information Fusion 8, 2007. Pp. 114-118.
  9. M. Choi, A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter, IEEE Trans. Geosci. Remote Sensing, 2006, vol.44, no.6, pp. 1672- 1682.
  10. W. Carper, M. Lillesand, and R. Kiefer, The use of intensity-hue-saturation transformations for merging SPOT pan and multi-spectral image data, Photogrammetric Engineering & Remote Sensing, 1990, vol.56, no.4, pp. 459-467.
  11. R. Schowengerdt, A. Remote Sensing: Models and Methods for Image Processing, (3rd. edition), Academic Press, ISBN 0-12-369407-8, San Diego, USA, 2007.
  12. W. Jin; L. Jianguo G. Tian, W. Jin-Wen and Y. Bingkun, Wavelet-based Remote Sensing Image Fusion with PCA and Feature Product, Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference, 2006, Pp.2053 -2057.
  13. D. Grasso, Applications of the IHS color transformation for 1:24,000-scale geologic mapping: A low cost SPOT alternative. Photogrammetric Engineering & Remote Sensing, 1993,. Vol. 59, No. 1, Pp. 73-80.
  14. W. Shi, C. Zhu, Y. Tian and J. Nichol, Wavelet based image fusion and quality assessment, International Journal of Applied Earth Observation and Geoinformation, 2005, Vol. 6, Pp. 241-251
  15. Y. Ling, M. Ehlers, E. Usery and M. Madden, FFT- enhanced HIS transform method for fusing high-resolution satellite images, ISPRS Journal of Photogrammetry and Remote Sensing, 2007, Vol.61, No.6, Pp.381-392, ISSN 0924-2716.
  16. E. Schetselaar, On preserving spectral balance in image fusion and its advantages for geological image interpretation, Photogrammetric Engineering and Remote Sensing, 2001,. Vol. 67, No. 8, Pp. 925-934.
  17. J. Nuñez., O. Otazu, A. Fors, V. Prades, Palà, and R. Arbiol, Multiresolution-based image fusion with additive wavelet decomposition, IEEE Transactions on Geosciences and Remote Sensing, 1999, Vol. 37, No. 3, Pp. 1204-1211.
  18. R. King and J. Wang, A wavelet based algorithm for pan sharpening Landsat 7 imagery. Geoscience and Remote Sensing Symposium, IGARSS '01. IEEE 2001 International, 2001, Vol. 2, 9-13, Pp.849 -851.
  19. Y. Chibaniand A. Houacine, Redundant versus orthogonal wavelet decomposition for multisensor image fusion. Pattern Recognition, 2003, Vol. 36, No. 4, Pp. 879-887.
  20. X. Xue, A Wang, H. Wang and F. Xiang, A fusion method of multi-spectral image and pan image based on NSCT and IHS transform, Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference, 2012, Pp. 640 -643.
  21. F. Al-Wassai, N. Kalyankar and A. Al-zuky, The IHS Transformations Based Image Fusion, Journal of Global Research in Computer Science, 2011a, Vol. 2, No. 5, pp. 70 -77.
  22. G. Xiao, J. Zhongliang; L. Jiamcun and L. Henry, Analysis of Color Distortion and Improvement for MS Image Fusion, Intelligent Transportation Systems, Proceedings IEEE, 2003, Vol. 1. Pp. 80 -85.
  23. J. Vrabel, Multispectral imagery band sharpening study, Photogrammetric Engineering and Remote Sensing, 1996, Vol. 62(9): Pp.1075-1083.
  24. O. Rockinger and T. Fechner, Pixel-Level Image Fusion: The Case of Image Sequences, Proceedings SPIE, 1998. Vol. 3374. Pp 378-388.
  25. L. Alparone, S. Baronti, A. Garzelli and F. Nencini, Landsat ETM+ and SAR Image Fusion Based on Generalized Intensity Modulation,. IEEE Transactions on Geoscience and Remote Sensing, 2004. Vol. 42, No. 12, pp. 2832-2839
  26. N. Prasad, S. Saran, S. Kushwaha and P. Roy,. Evaluation Of Various Image Fusion Techniques And Imaging Scales For Forest Features Interpretation,. Current Science, 2001, Vol. 81, No. 9, pp.1218
  27. D. Amarsaikhan, H. Blotevogel, J. van Genderen, M. Ganzorig, R. Gantuya and B. Nergui, Fusing high- resolution SAR and optical imagery for improved urban land cover study and classification, International Journal of Image and Data Fusion, 2010, Vol. 1, No. 1, Pp. 83-97.
  28. I. Parcharidis and L. Tani, Landsat TM and ERS Data Fusion: A Statistical Approach Evaluation for Four Different Methods,. 0-7803-6359-0/00/ 2000 IEEE, 2000, pp.2120-2122.
  29. Y. Zhang, Problems in the fusion of commercial high resolution satellite images as well as Landsat 7 images and initial solutions, International Archives of Photogrammetry and Remote Sensing, 2002, Vol. 34, Part 4: GeoSpatial Theory, Processing and Applications, Ottawa.
  30. P. Chavez and A. Kwarteng, Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis, Photogramm. Eng. Remote Sens., 1989, vol. 55, pp. 339-348.
  31. Y. Luo, R. Liu, and Y. Zhu, Fusion of Remote Sensing Image Base on The PCA+Atrous Wavelet Transform. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008, .Vol. XXXVII. Part B7. Beijing. PP. 1155-1158.
  32. V. Shah, N. Younan and R. King, An efficient Pan- sharpening method via a combined Adaptive PCA approach and Contourlets, IEEE Transactions on Geoscience and remote sensing, 2008,VOL. 46, NO. 5
  33. S.Singh, A. Gyaourova,, G. Bebis, and Pavlidis, I., (2004). Infrared and visible image fusion for face recognition. In Biometric Technology for Human Identification, 12-13 April 2004, USA, Pp. 585-596.
  34. S. Kumar and S. Muttan, PCA Based Image Fusion. The Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII. Orlando (Kissimmee), FL, USA, 2006.
  35. V. Tsagaris, and V. Anastassopoulos, Global measure for assessing image fusion methods. Optical Engineering, 45(2). Vision Research: High Speed Digital Imaging Systems, 2006. Retrieved February 22, 2007.
  36. V. Petrovic, C. Xydeas, Multiresolution image fusion using cross band selection, Proc. SPIE 39 1999, Pp. 319 - 326.
  37. H. Li, B. Manjunath, S. Mitra, Multisensor image fusion using the wavelet transform, Graphical Models Image Process. 57 (3), 1995, Pp. 235 -245.
  38. Y. Chibani, A. Houacine, Redundant versus orthogonal wavelet decomposition for multisensor image fusion,. Pattern Recognition, 2003, Vol. 36, Pp. 879-887.
  39. G. Pajares, A wavelet-based image fusion tutorial, Pattern Recognition. 2004. Vol. 37, pp: 1855-1872.
  40. G. Simone, A. Farina, F. Morabito, S. Serpico and L. Bruzzone, Image Fusion Techniques for Remote Sensing Applications, Information. Fusion, 2002, 3(1), pp: 3-15.
  41. S. Wenzhong, Z. ChangQing, T. Yan and N. Janet, Wavelet-based image fusion and quality assessment, International Journal of Applied Earth Observation and Geoinformation, 2005, ( 6),pp: 241-251.
  42. S. Ioannidou and V. Karathanassi, Investigation of the Dual-Tree Complex and Shift-Invariant DiscreteWavelet Transforms on Quickbird Image Fusion. IEEE Geoscience and Remote Sensing Letters, 2007, Vol.4, No.4, (January 2007), pp.166-170, ISSN 1545-598X
  43. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
  44. I. Daubechies, Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math. 41 , 1988, Pp. 909 - 996.
  45. D.Yocky, Image merging and data fusion by means of the discrete two-dimensional wavelet transform, 1995, J. Opt. Soc. Am A 12(9), Pp. 1834-1841.
  46. Y. Zhang and G. Hong, An HIS and wavelet integrated approach to improve pansharpening visual quality of natural colour IKONOS and QuickBird images. Information Fusion 6(3), 2005, Pp.225-234.
  47. G. Pajares and J. de la Cruz. A wavelet-based image fusion tutorial. Pattern Recognition, 2004, Vol.37, No.9, pp.1855-1872, ISSN 0031-3203
  48. M. Gonzalez-Audicana, X. Otazu, O. Fors, and A. Seco, Comparison between Mallat' sand the 'à trous' discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing 26(3), 2005, Pp.595-614.
  49. M. Gonzalez-Audicana, J. Saleta, R. Catalan and R. Garcia, Fusion of multispectral and panchromatic images using improved HIS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing 42(6), 2004. Pp.1291-1299.
  50. A. Garzelli, and F. Nencini, Interband structure modeling for pansharpening of very high-resolution multispectral images. Information Fusion 6(3), 2005, Pp.213-224.
  51. X. OtazuGonzalez-Audicana, O. Fors, and J. Nunez Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods, IEEE Transactions on Geoscience and Remote Sensing 43(10), 2005, Pp. 2376-2385.
  52. S. Yang, M. Wang and L. Jiao, Fusion of multispectral and panchromatic images based on support value transform and adaptive principal component analysis,.
  53. Elsevier,. Information Fusion, 2012, Vol. 13, Issue 3, July 2012, Pp. 177-184
  54. Q. Miao and B. Wang, A novel image fusion method using WBCT and PCA, ,Chinese optics letters, 2008,vol.6, no.2, pp.l04-107.
  55. J. Wu, J. Liu, J. Tian and B. Yin, Wavelet-based Remote Sensing Image Fusion with PCA and Feature Product,. Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation June 25 -28, 2006, Luoyang, China. Pp. 2053-2057.
  56. C. YUNHAO, D. LEI, L. JING, L. XIAOBING and S. PEIJUNA, new wavelet-based image fusion method for remotely sensed data, International Journal of Remote Sensing, 2006, Vol. 27, No. 7, 10 April 2006, 1465-1476.

Related papers

A STUDY OF IMAGE FUSION TECHNIQUES IN REMOTE SENSING
Farhad Samadzadegan

The amount and variety of remote sensing imagery of varying spatial resolution is continuously increasing and techniques for merging images of different spatial and spectral resolution became widely accepted in practice. This practice, known as data fusion, is designed to enhance the spatial resolution of multispectral images by merging a relatively coarse-resolution image with a higher resolution panchromatic image taken of the same geographic area. This study examines fused images and their ability to preserve the spectral and spatial integrity of the original image. The mathematical formulation of ten data fusion techniques is worked out in this paper. Included are colour transformations, wavelet techniques, gradient and Laplacian based techniques, contrast and morphological techniques, feature selection and simple averaging procedures. Most of theses techniques employ hierarchical image decomposition for fusion. IRS-1C and ASTER images are used for the experimental investigations. The panchromatic IRS-1C image has around 5m pixel size, the multispectral ASTER images are at a 15m resolution level. For the fusion experiments the three nadir looking ASTER bands in the visible and near infrared are chosen. The concept for evaluating the fusion methods is based on the idea to use a reduced resolution of the IRS-1C image data at 15m resolution and of the ASTER images at 45m resolution. This maintains the resolution ratio between IRS and ASTER and allows comparing the image fusion result at the 15m resolution level with the original ASTER images. This statistical comparison reveals differences between all considered fusion concepts.

View PDFchevron_right
Assessment of Different Fusion Methods Applied to Remote Sensing Imagery
Shivaprakash Koliwad

2012

Image fusion is a formal framework for combining and utilizing data originating from different sources. It aims at producing high resolution multispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) image. This fused image must contain more interpretable information than can be gained by using the original image. Ideally the fused image should not distort the spectral characteristics of multispectral data as well as, it should retain the basic colour content of the original data. There are many data fusion techniques that can be used which include Principal Component Analysis (PCA), Brovey Transform (BT), Multiplicative Transform (MT) and Discrete Wavelet Transform (DWT). One of the major problems associated with a data fusion technique is how to assess the quality of the fused (spatially enhanced) MS image. This paper presents a comprehensive analysis and evaluation of the most commonly used data fusion techniques. The performance of...

View PDFchevron_right
Survey on image fusion techniques used in remote sensing
Kalyani S. Ulabhaje, Sujata More, Ijariit Journal

Image fusion in remote sensing has emerged as a sought-after protocol because it has proven beneficial in many areas, especially in studies of agriculture, environment, and related fields. Simply put, image fusion involves garnering all pivotal data from many images and then merging them in fewer images, ideally into a solitary image. This is because this one fused image packs all the pertinent information and is more correct than any picture extracted from one solitary source. It also includes all the data that is required. Additional advantages are: it lessens the amount of data and it creates images that are appropriate and that can be understood by humans and machines. This paper reviews the three image fusing processing levels, which include feature level, decision level, and pixel level. This paper also dwells upon image fusion methods that fall under four classes: MRA, CS, model-based solutions and hybrid and shows how each class has some distinct advantages as well as drawbacks.

View PDFchevron_right
Different Levels of Image Fusion Techniques in Remote Sensing Applications and Image Classification
Pad Mini

Index Terms-image fusion, data fusion, remote sensing, image processing, signal processing. Visual Sensor, DCT.

View PDFchevron_right
Comparative Study of Different Intensity Hue Saturation based Image Fusion Techniques in Remote Sensing
Ms. Jinju Joy LMCST

2020

Remote Sensing is the process of gathering information about a particular area or an object from a distance usually from an aircraft or satellites. There are two types of sensors that are popular among remote sensing (RS) applications, one capable of providing images having good spatial resolution known as the Panchromatic Image (PAN) and another with a good spectral resolution, known as the Multispectral image (MS). Even though spatial information of the PAN image is relatively high, the associated spectral information is less. Similarly, for the MS image, the spectral information is more while lacking spatial information. This accounts for the need for a technique known as image fusion. Combining the information of an object or an area obtained from different sensors to produce a single fused image is the main concept behind image fusion. This paper is written to study the different image fusion techniques based on Intensity Hue Saturation (IHS) and to have a comparative study of ...

View PDFchevron_right

Related topics

  • Computer Science
  • Image Processing
  • Remote Sensing
  • Metaheuristics (Operations Resea...
  • Remote sensing and GIS
  • Image fusion
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved