A Comparative Analysis of Existing Satellite Image Enhancement Techniques for Effective Visual Display

2019 ◽  
Vol 16 (9) ◽  
pp. 4003-4007 ◽  
Author(s):  
Neetu Manocha ◽  
Rajeev Gupta
Keyword(s):  
Comparative Analysis ◽  
Image Enhancement ◽  
High Frequency ◽  
Satellite Images ◽  
Satellite Image ◽  
Research Work ◽  
Visual Display ◽  
Image Resolution ◽  
High Image Resolution

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 satellite image enhancement techniques to reduce the blur of an image on the basis of accuracy and response time. The aim of research work is to eliminate the noise without losing high frequency details and to enhance the image for effective visual display.

scholarly journals Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

2019 ◽  
Vol 224 ◽  
pp. 04010
Author(s):  
Viacheslav Voronin
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Satellite Images ◽  
Color Image ◽  
State Of The Art ◽  
Satellite Image ◽  
Detailed Model ◽  
Global Contrast ◽  
Enhancement Algorithm

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

scholarly journals A Survey on Intensification Techniques used in Satellite Images

2021 ◽  
Vol 9 (VII) ◽  
pp. 2242-2245
Author(s):  
Bipin D. Tamkhane
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Research Work ◽  
Research Area ◽  
Research Areas ◽  
Image Intensification ◽  
Human Eyes

An image intensification is a required methodology in field of Satellite image research area. The images taken through satellite are captured from very longer distance and because of this images having garbling and noise as lots of airy barriers are present in between the path. The usage of Satellite images is very diverse in research areas like astrological studies, geographical studies, study of geoscience, etc. Nowadays, after taking a snapshot of an image, some of the radiometric or geometric based enhancement techniques are applied on the images taken from satellite but these techniques do not fulfill the requirements in all application areas. This is what there is a need to improvise the quality of an image before it is being actually used. The main objective behind this research work is to understand the different methodologies used in intensification of satellite images and how can we perform more improvements to existing techniques so these type of images which are taken from satellite are intelligible to the human eyes. The meaning of intensification in term of image is nothing but the altering of a look and feel of the image in a way that the information contained by that image is more readily intelligible visually.

scholarly journals APPLICATION OF SEMANTIC SEGMENTATION WITH FEW LABELS IN THE DETECTION OF WATER BODIES FROM PERUSAT-1 SATELLITE’S IMAGES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 183-187
Author(s):  
J. Gonzalez ◽  
K. Sankaran ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Semantic Segmentation ◽  
Image Resolution ◽  
New Approach ◽  
Knowledge Based ◽  
Training Samples ◽  
Water Surfaces ◽  
High Image Resolution ◽  
Very High

Abstract. Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved.

scholarly journals A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

2020 ◽  
Vol 63 (6) ◽  
pp. 913-926
Author(s):  
T Mahalakshmi ◽  
Alluri Sreenivas
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Adaptive Filter ◽  
Optimization Algorithm ◽  
Fuzzy System ◽  
Satellite Images ◽  
Satellite Image ◽  
Similarity Index ◽  
Noise Correction

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.

scholarly journals An Investigation on Water Quality of Darlik Dam Drinking Water using Satellite Images

2010 ◽  
Vol 10 ◽  
pp. 1293-1306 ◽  
Author(s):  
Erhan Alparslan ◽  
H. Gonca Coskun ◽  
Ugur Alganci
Keyword(s):  
Water Quality ◽  
Satellite Images ◽  
Satellite Image ◽  
Water Reservoir ◽  
Acquisition Time ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Dissolved Matter ◽  
Image Acquisition Time

Darlik Dam supplies 15% of the water demand of Istanbul Metropolitan City of Turkey. Water quality (WQ) in the Darlik Dam was investigated from Landsat 5 TM satellite images of the years 2004, 2005, and 2006 in order to determine land use/land cover changes in the watershed of the dam that may deteriorate its WQ. The images were geometrically and atmospherically corrected for WQ analysis. Next, an investigation was made by multiple regression analysis between the unitless planetary reflectance values of the first four bands of the June 2005 Landsat TM image of the dam and WQ parameters, such as chlorophyll-a, total dissolved matter, turbidity, total phosphorous, and total nitrogen, measured at satellite image acquisition time at seven stations in the dam. Finally, WQ in the dam was studied from satellite images of the years 2004, 2005, and 2006 by pattern recognition techniques in order to determine possible water pollution in the dam. This study was compared to a previous study done by the authors in the Küçükçekmece water reservoir, also in Istanbul City.

scholarly journals Fractional Contrast Stretching for Image Enhancement of Aerial and Satellite Images

2019 ◽  
Vol 63 (6) ◽  
pp. 60411-1-60411-11
Author(s):  
Thaweesak Trongtirakul ◽  
Werapon Chiracharit ◽  
Susan Imberman ◽  
Sos Agaian
Keyword(s):  
Image Enhancement ◽  
Satellite Images ◽  
Satellite Image ◽  
Weather Conditions ◽  
The United States ◽  
Local Contrast ◽  
Pixel Resolution ◽  
New Techniques ◽  
Intensity Saturation ◽  
Contrast Stretching

Abstract Aerial and satellite photographs suffer from uncontrollable weather conditions. Frequently, illumination of the same region can be totally different. This is usually due to shadowing self-obstruction or light reflection. Existing image enhancement methods fail to improve hidden details and local contrast at the same visualization level. They are not developed to enhance through local dark or light regions simultaneously. Also, the current aerial and satellite image enhancement methods have several limitations. For instance, these include intensity saturation, non-uniform brightness, halo effect, blur edges, and so on. This article introduces a fractional contrast stretching concept for aerial and satellite image enhancement based on a novel automated non-uniform luminance normalization that is not provided by the user as input parameters. The introduced approach contains several new techniques: (i) no reference non-linearly fractional contrast stretching with automatic non-uniform luminance normalization and (ii) non-linearly local contrast stretching for spatial details and edge sharpening. The proposed algorithm was tested on the orthorectified aerial photograph database with a pixel resolution of 1 meter or finer from across the United States during 2000‐2016. The simulation results illustrate the efficiency of the proposed algorithm and its advantages for cutting-edge aerial and satellite image enhancement, resulting in visualization quality.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

scholarly journals Improved Color Satellite Image Segmentation Using Tsallis Entropy and Granular Computing

2017 ◽  
Vol 6 (4) ◽  
pp. 293
Author(s):  
Jagan Kumar. N ◽  
Agilandeeswari. L ◽  
Prabukumar. M
Keyword(s):  
Satellite Images ◽  
Granular Computing ◽  
Search Algorithm ◽  
Satellite Image ◽  
Research Work ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Computing Methods ◽  
Tsallis Entropy ◽  
Threshold Values

<p>The research work is to improve the segmentation of the color satellite images. In this proposed method the color satellite image can be segmented by using Tsallis entropy and granular computing methods with the help of cuckoo search algorithm. The Tsallis and granular computing methods will used to find the maximum possibility of threshold limits and the cuckoo search will find the optimized threshold values based on threshold limit that is calculated by the Tsallis entropy and granular computing methods and the multilevel thresholding  will used for the segmentation of color satellite images based on the optimized threshold value that will find by this work and these methods will help to select the optimized threshold values for multiple thresholding effectively.<strong></strong></p>

scholarly journals GEOMETRIC QUALITY EVALUATION METHOD FOR MULTI-VIEW SATELLITE IMAGES IN 3D RECONSTRUCTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 533-539
Author(s):  
H. Yi ◽  
X. Chen ◽  
D. Wang ◽  
S. Du ◽  
B. Xu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Satellite Images ◽  
Evaluation Method ◽  
Satellite Image ◽  
Geometric Distortion ◽  
Geometric Quality ◽  
Deviation Coefficient ◽  
Ground Grid ◽  
Straight Lines

Abstract. The quality of the 3D model reconstructed using multi-view satellite image depends on the quality of the image. To evaluate the geometric quality of the satellite image, we proposed a method to evaluate the geometric distortion for satellite images and defined the deviation coefficient as a metric to evaluate the bending degree of a curve. After projecting a ground grid consisting of straight lines into the image space, the geometric distortion of the image can be evaluated quantitatively by calculating the deviation coefficients of the projection trajectories. Experiments have been carried out with three datasets obtained by JiLin-1, GaoFen-2, and WorldView-3 respectively. The results show that the proposed method can used to evaluate the geometric quality of satellite images effectively, and this evaluation method will be useful in image selecting in 3D reconstruction using multi-view satellite images.

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