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Relevant bibliographies by topics / Satellite image enhancement

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Academic literature on the topic 'Satellite image enhancement'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Satellite image enhancement"

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Mrs., Shital G. More, and L.K.Chouthmol Prof. "IMAGE ENHANCEMENT FOR SATELLITE IMAGE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 401–5. https://doi.org/10.5281/zenodo.47559.

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In this work, proposing an image resolution enhancement technique which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different sub bands. Then the three high frequency sub band images have been interpolated using bicubic interpolation. The high frequency sub bands obtained by SWT of the input image are being incremented into the interpolated high frequency sub bands in order to correct the estimated coefficients. In parallel, the input image is also interpolated separately Discrete wavelet transform (DWT) is one of the recent

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Bogireddy, Gari Sairekha. "An improved technique for enhancement of satellite image." i-manager’s Journal on Image Processing 11, no. 2 (2024): 10. http://dx.doi.org/10.26634/jip.11.2.20816.

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In the age of artificial intelligence, remote sensing and especially satellite imagery are gaining widespread interest among the computer science community in their efforts to enable machines to recognize their environment through satellite image classification. Imaging satellites provide images of Earth that are collected, analyzed, and processed for both civil and military purposes. Satellite images are an important source of data, captured by artificial satellites orbiting the Earth. These images are susceptible to noise and irregular illumination, which can affect their quality. This paper

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Syed, Nazeeburrehman, and Ali Hussain Mohameed. "Image Resolution Enhancement Using Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 354–56. https://doi.org/10.11591/ijeecs.v9.i2.pp354-356.

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In this project, interruption based image resolution enhancement technique using Discrete Wavelet Transform (DWT) with high-frequency sub bands obtained is proposed. Input images are decomposed by using DWT in this proposed enhancement technique. Inverse DWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio

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Manocha, Neetu, and Rajeev Gupta. "A Comparative Analysis of Existing Satellite Image Enhancement Techniques for Effective Visual Display." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 4003–7. http://dx.doi.org/10.1166/jctn.2019.8285.

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Due to environment untidiness and inappropriate setting or dealing of camera, a satellite image contains blur or other types of noises. These images are captured by satellites consist lots of information about the surface of earth or other planets. But, due to blur or noise, the quality of these images is degraded. Now days, there are many fields in which satellite images are used, which effects the environment. The accuracy and effective visual display of satellite images with high image resolution using CBIR technique is major concern. This paper presents a comparative analysis of existing s

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C., Periyasamy. "Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform." Bulletin of Electrical Engineering and Informatics 6, no. 4 (2017): 334–36. https://doi.org/10.11591/eei.v6i4.861.

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Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to

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Suryamani, Singh*1 &. Mrs. Priyanka Gaur2. "REVIEW PAPER ON UNDERWATER AND SATELLITE IMAGE ENHANCEMENT USING AUTO THRESHOLD METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 429–34. https://doi.org/10.5281/zenodo.1247299.

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Now days applications require various kinds of images as sources of information for interpretation and inspection. Image enhancement is method of applying different alterations to an input image to make the resultant image more pleasing or to provide a better transform presentation for future automated image processing techniques. Many images like medical images, images of satellites, and even real life photographs suffer from poor sharpness and noisy effects. This is essential to enhance the contrast and remove the noise to increase picture standard. One is presenting a review on various imag

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Goni, Ibrahim, Yusuf Musa Malgwi, and Asabe Sandra Ahmadu. "Satellite Image Enhancement Using Histogram Equalization." Electrical Science & Engineering 5, no. 1 (2023): 9–20. http://dx.doi.org/10.30564/ese.v5i1.5234.

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Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and

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Hussein, Dalia A., Mohamed A. Yousef, Hassan A. Abdel-Hak, and Yasser G. Mostafa. "SATELLITE IMAGE ENHANCEMENT USING DEEP LEARNING AND GIS INTEGRATION: A COMPREHENSIVE REVIEW." Rudarsko-geološko-naftni zbornik 40, no. 3 (2025): 95–118. https://doi.org/10.17794/rgn.2025.3.8.

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A comprehensive review of 32 studies (20 journals, 11 proceedings, and one book chapter) published from 2016 to 2023 in the fields of deep learning (DL), image enhancement, super-resolution image, and Geographic Information System (GIS) is presented, focusing on the integration of DL methodologies with GIS to improve the quality of satellite images. The review summarizes the background, principles, enhancement quality, speed, and advantages of these technologies, comparing their performance based on metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared

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Periyasamy, C. "Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform." Bulletin of Electrical Engineering and Informatics 6, no. 4 (2017): 334–36. http://dx.doi.org/10.11591/eei.v6i4.861.

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Abstract:

Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to

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Wang, Ruizhe, and Wang Xiao. "Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion." Journal of Mathematics 2022 (January 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1029247.

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Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. Accor

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Dissertations / Theses on the topic "Satellite image enhancement"

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Li, Mao Li. "Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669.

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I-ChenTSAI and 蔡易澄. "Adaptive Contrast Enhancement for Satellite Image Registration." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/42uqq3.

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碩士<br>國立成功大學<br>測量及空間資訊學系<br>106<br>Remote sensing researches using optical satellite images have been addressed for years. Image matching is one of the key techniques applied to remote sensing applications. For instance, image fusion and orthogonal image generation require the step of image matching in data processing. This study focuses on image matching for optical satellite images. The goal is to improve matching results in terms of algorithm robustness and the number of matched feature pairs. The matching algorithm proposed in this study is based on Speed Up Robust Feature (SURF). The fea

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KHANNA, CHINTAN. "SATELLITE IMAGE CONTRAST ENHANCEMENT USING MODIFIED HISTOGRAM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15235.

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This project presents a study of various Histogram Equalisation based Contrast Enhancement (CE) techniques followed by the proposal of a novel CE algorithm for satellite and aerial images. The algorithm is referred as Contour Based Histogram Equalisation (CBHE). The algorithm presents a novel method to capture the structural property of an image using the contour of the image. The algorithm addresses the inherent drawbacks of HE viz. artefacts and saturation by decreasing the contribution of high probability pixels in the histogram and increasing that of low probability pixels. Finally the alg

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Wijaya, Candera, and 洪若彬. "Spatial Local Contrast Enhancement of Satellite Images." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/85728602006669517385.

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碩士<br>國立中央大學<br>土木工程研究所<br>98<br>The main purpose of image enhancement is to increase the contrast in order to bring out hidden details of an image. Therefore, the image enhancement generally is an important process to have a better image quality for visual applications. In the global approach, the enhancement methods generally use a single mapping function to enhance the whole image. However, a single enhancement mapping function can not improve image contrast satisfactorily since the contrast of an object is interfered by the whole image. Naturally, it is difficult to find a good mapping fun

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Cruz, Cheri Ann. "Satellite image enhancements, lineament identification and quantitative comparison with fracture data, central New York State." 2005. http://proquest.umi.com/pqdweb?did=974425831&sid=5&Fmt=2&clientId=39334&RQT=309&VName=PQD.

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Thesis (M.S.)--State University of New York at Buffalo, 2005.<br>Title from PDF title page (viewed on Apr. 13, 2006) Available through UMI ProQuest Digital Dissertations. Thesis adviser: Jacobi, Robert D. Includes bibliographical references.

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Books on the topic "Satellite image enhancement"

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Booth, David Mark. The enhancement and restoration of infrared astronomical satellite images. Oxford Polytechnic, 1987.

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Optical Satellite Signal Processing and Enhancement. SPIE Press, 2013.

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Image processing methods: Procedures in selection, registration, normalization and enhancement of satellite imagery in coastal wetlands. U.S. Geological Survey, Center for Coastal Geology, 1997.

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Book chapters on the topic "Satellite image enhancement"

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. "Satellite Image Enhancement and Analysis." In Satellite Image Analysis: Clustering and Classification. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2_2.

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Giri, Vivek, and Sudhriti Sen Gupta. "Satellite Image Enhancement and Restoration." In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_28.

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Sharma, Nitin, and Om Prakash Verma. "A Novel Fuzzy Based Satellite Image Enhancement." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2107-7_38.

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Pandey, Rudra Narayan, Shreyas Shubhankar, Bibhudendra Acharya, and Sudhansu Kumar Mishra. "Generative Adversarial Network-Based Satellite Image Enhancement." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1906-0_43.

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Darbari, Priyanka, and Manoj Kumar. "Satellite Image Enhancement Techniques: A Comprehensive Review." In Proceedings of International Conference on Communication and Artificial Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0976-4_36.

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Lasaponara, Rosa, and Nicola Masini. "Image Enhancement, Feature Extraction and Geospatial Analysis in an Archaeological Perspective." In Satellite Remote Sensing. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8801-7_2.

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Journaux, Ludovic, Irène Foucherot, and Pierre Gouton. "Multispectral Satellite Images Processing through Dimensionality Reduction." In Signal Processing for Image Enhancement and Multimedia Processing. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-72500-0_6.

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Biswas, Biswajit, and Biplab Kanti Sen. "Satellite Image Contrast Enhancement Using Fuzzy Termite Colony Optimization." In Hybrid Metaheuristics for Image Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77625-5_5.

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Asokan, Anju, and J. Anitha. "Artificial Bee Colony-Optimized Contrast Enhancement for Satellite Image Fusion." In Remote Sensing and Digital Image Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24178-0_5.

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Janghel, Rekh Ram, Saroj Kumar Pandey, Aayush Jain, Aditi Gupta, and Avishi Bansal. "Satellite Image Enhancement and Restoration Using RLS Adaptive Filter." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_49.

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Conference papers on the topic "Satellite image enhancement"

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Hamidi, Shahrokh. "Joint Image De-Noising and Enhancement for Satellite-Based SAR." In 2024 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES). IEEE, 2024. https://doi.org/10.1109/icares64249.2024.10768091.

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Kim, Geunhwan, Bong-seok Kim, Youngdoo Choi, and Sangdong Kim. "Noise Reduction-Based Image Enhancement Using Transformer Networks for Satellite SAR Radar Recognition." In 2024 Conference on AI, Science, Engineering, and Technology (AIxSET). IEEE, 2024. https://doi.org/10.1109/aixset62544.2024.00056.

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Alam, Md Sorwar, and Rafiqul Islam. "Exploring Deep Feature Loss in Generative Adversarial Network for Satellite Image Resolution Enhancement." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013925.

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Avkopashvili, Irakli. "VOLCANOTECTONIC STRUCTURES AND LINEAMENT PRELIMINARY IDENTIFICATION RESULTING FROM THE APPLICATION OF REMOTE SENSING AND GIS IN BOLNISI ORE DISTRICT, GEORGIA." In 24th SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/1.1/s01.18.

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The Bolnisi Ore District (Georgia), situated within the Tethyan-Eurasian metallogenic belt, embodies a multifaceted geological milieu marked by the intricate interplay of volcanic and tectonic processes, predominantly delineated by Late Cretaceous volcanic and sedimentary sequences entrenched between the Khrami and Loki Variscan crystalline massifs. Noteworthy for its rich mineral endowment, the Bolnisi Ore District harbors a plethora of ore deposits, with several operational mining sites situated within its bounds. This preliminary research aimed mainly by means of the remote sensing to inves

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Tadić, Vladimir, Juvenal Rodríguez-Reséndiz, and Ákos Odry. "Weather satellite image enhancement using homomorphic filtering." In Entrepreneurship, engineering and management: Climate change as an engineering challenge, Zrenjanin, 26.04.2025. Visoka tehnička škola strukovnih studija, Zrenjanin, 2024. https://doi.org/10.5937/pim25265t.

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This paper presents an enhancement method for weather satellite images using a homomorphic filtering procedure. Homomorphic filtering is a technique applied in the frequency domain that enhances images by compressing their intensity range and simultaneously improving their contrast. Prior to applying homomorphic filtering, a median filter is used to remove noise from the input image. The results demonstrate that the proposed procedure successfully improves the visibility of weather satellite images.

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Kurekin, Andrei A., Vladimir V. Lukin, Irina S. Lukina, and Alexander A. Zelensky. "Image enhancement using signal spectral abundance." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227201.

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Lalitha, V. P., Shanta Rangaswamy, C. R. Gouthami, T. Jai Balaj, Pramod Kumar, and Rajashekhar G. Dolli. "Satellite Image Enhancement Using Neural Networks." In 2018 3rd International Conference on Inventive Computation Technologies (ICICT). IEEE, 2018. http://dx.doi.org/10.1109/icict43934.2018.9034421.

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Rahmadi, Deddy, and Silvia Rachmawati. "Landsat Satellite Image Quality Improvement Using Discrete Cosine Transform Method." In The 6th International Conference on Science and Engineering. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-bvfs09.

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Landsat satellite images are images that represent the ocean and land areas of the earth. Image data can be used for various purposes such as environmental analysis, remote sensing, mapping, and others. However, the quality of Landsat imagery is often unsatisfactory due to interference or noise from sources such as sensors, transmission, atmosphere, and storage. Therefore, they can reduce the contrast, sharpness, and information of landsat satellite images. Some of these disturbances prevent people from obtaining clear geographical locations. In order to overcome this problem, an effective and

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Kenneth, A. Alphonse John, and F. Ramesh Dhanaseelan. "An Extensive Survey on Satellite Image Enhancement." In 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2018. http://dx.doi.org/10.1109/icssit.2018.8748631.

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Ahire, Rina B., and V. S. Patil. "Overview of satellite image resolution enhancement techniques." In 2013 Tenth International Conference on Wireless and Optical Communications Networks - (WOCN). IEEE, 2013. http://dx.doi.org/10.1109/wocn.2013.6616261.

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