scholarly journals Flood Detection/Monitoring Using Adjustable Histogram Equalization Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fakhera Nazir ◽  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor ◽  
Fahim Arif
Keyword(s):  
State Of The Art ◽  
User Interaction ◽  
Histogram Equalization ◽  
Monitoring Technique ◽  
Output Quality ◽  
Flood Monitoring ◽  
Equalization Technique ◽  
Flood Detection ◽  
Processing Steps ◽  
Flood Maps

Flood monitoring technique using adjustable histogram equalization is proposed. The technique overcomes the limitations (overenhancement, artifacts, and unnatural look) of existing technique by adjusting the contrast of images. The proposed technique takes pre- and postimages and applies different processing steps for generating flood map without user interaction. The resultant flood maps can be used for flood monitoring and detection. Simulation results show that the proposed technique provides better output quality compared to the state of the art existing technique.

Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning

2020 ◽  
Author(s):  
Binayak Ghosh ◽  
Mahdi Motagh ◽  
Mahmud Haghshenas Haghighi ◽  
Setareh Maghsudi
Keyword(s):  
Machine Learning ◽  
Open Water ◽  
Semantic Segmentation ◽  
Flood Mapping ◽  
Assistance Programs ◽  
Amplitude Data ◽  
Flood Monitoring ◽  
Sar Data ◽  
Flood Detection ◽  
Flood Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information. </span></span><span xml:lang="EN-US" data-contrast="auto"><span>It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the </span></span><span xml:lang="EN-US" data-contrast="auto"><span>spatio</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-temporal information from the SAR images having both </span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensity</span></span><span xml:lang="EN-US" data-contrast="auto"><span> as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps.</span></span><span> </span></p>

scholarly journals Brief Communication: Contrast stretching and histogram smoothness based flood detection

2014 ◽  
Vol 2 (8) ◽  
pp. 5037-5045
Author(s):  
F. Nazir ◽  
M. M. Riaz ◽  
A. Ghafoor ◽  
F. Arif
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Histogram Smoothing ◽  
Flood Detection ◽  
Contrast Stretching ◽  
Difference Images ◽  
Processing Steps ◽  
Flood Maps ◽  
Aperture Radar

Abstract. Synthetic aperture radar images used for flood detection often have degraded contrast, which consequently leads to inaccurate flood maps. A technique for flood detection based on contrast stretching and histogram smoothing is proposed. The proposed technique applies different processing steps (based on contrast stretching and histogram smoothness) on pre, post and difference images to improve visualization by maintaining the natural smoothness.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

scholarly journals Investigating the role of global histogram equalization technique for 99mTechnetium-Methylene diphosphonate bone scan image enhancement

2017 ◽  
Vol 32 (4) ◽  
pp. 283 ◽  
Author(s):  
AnilKumar Pandey ◽  
ParamDev Sharma ◽  
Pankaj Dheer ◽  
GirishKumar Parida ◽  
Harish Goyal ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Bone Scan ◽  
Histogram Equalization ◽  
Methylene Diphosphonate ◽  
Equalization Technique

scholarly journals Variational Histogram Equalization for Single Color Image Defogging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Li Zhou ◽  
Du Yan Bi ◽  
Lin Yuan He
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Histogram Equalization ◽  
Degradation Model ◽  
Image Defogging ◽  
Hsi Color Space ◽  
General Variation ◽  
Bad Weather ◽  
Physical Degradation

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

An Image De-Noising Method Based on Intensity Histogram Equalization Technique for Image Enhancement

2018 ◽  
pp. 311-323
Author(s):  
Shantharajah S. P. ◽  
Ramkumar T ◽  
Balakrishnan N
Keyword(s):  
Image Enhancement ◽  
Mapping Function ◽  
Histogram Equalization ◽  
Noise Removal ◽  
False Positive Error ◽  
Intensity Histogram ◽  
Efficient Processing ◽  
Equalization Technique ◽  
Interactive Procedures ◽  
Different Levels

Image enhancement is a quantifying criterion for sharpening and enhancing image quality, where many techniques are empirical with interactive procedures to obtain précised results. The proposed Intensity Histogram Equalization (IHE) approach conquers the noise defects that has a preprocessor to remove noise and enhances image contrast, providing ways to improve the intensity of the image. The preprocessor has the mask production, enlightenment equalization and color normalization for efficient processing of the images which generates a binary image by labeling pixels, overcomes the non-uniform illumination of image and classifies color capacity, respectively. The distinct and discrete mapping function calculates the histogram values and improves the contrast of the image. The performance of IHE is based on noise removal ratio, reliability rate, false positive error measure, Max-Flow Computational Complexity Measure with NDRA and Variation HOD. As the outcome, the different levels of contrast have been significantly improved when evaluated against with the existing systems.

scholarly journals Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

2018 ◽  
Vol 10 (8) ◽  
pp. 1256 ◽  
Author(s):  
Mitchell Goldberg ◽  
Sanmei Li ◽  
Steven Goodman ◽  
Dan Lindsey ◽  
Bill Sjoberg ◽  
...  
Keyword(s):  
United States ◽  
Emergency Management ◽  
Spatial Resolution ◽  
Infrared Imaging ◽  
High Spatial Resolution ◽  
Economic Loss ◽  
The United States ◽  
Flood Monitoring ◽  
Flooded Areas ◽  
Flood Maps

Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

scholarly journals New Combined Technique for Fingerprint Image Enhancement

2016 ◽  
Vol 11 (1) ◽  
pp. 222 ◽  
Author(s):  
Alaa Ahmed Abbood ◽  
Mohammed Sabbih Hamoud Al-Tamimi ◽  
Sabine U. Peters ◽  
Ghazali Sulong
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Fingerprint Image ◽  
Pixel Intensity ◽  
Improve Image Quality ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Fingerprint Image Enhancement ◽  
Normalization Process ◽  
Combined Technique

This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one pixel-wide lines. Finally, the Fusion technique was used to merge the results of the Histogram Equalization process with the Skeletonization process to obtain the new high contrast images. The proposed method was tested in different quality images from National Institute of Standard and Technology (NIST) special database 14. The experimental results are very encouraging and the current enhancement method appeared to be effective by improving different quality images.

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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