scholarly journals Image Enhancement Methods for Remote Sensing: A Survey

2021 ◽  
Author(s):  
Nur Huseyin Kaplan ◽  
Isin Erer ◽  
Deniz Kumlu
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Hybrid Methods ◽  
Input Image ◽  
Transform Domain ◽  
Histogram Modification ◽  
Contrast Gain ◽  
Gain Enhancement ◽  
Color Preservation

The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.

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.

scholarly journals Reduction based Histogram Equalization Technique for Image Enhancement

2019 ◽  
Vol 8 (11S) ◽  
pp. 1268-1271
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Input Image ◽  
Subjective Quality ◽  
Lower Intensity ◽  
Equalization Technique ◽  
Human Interpretation

Here the proposed scheme mainly emphasizes the procedure of histogram equalization of images in more efficient way. Histogram equalization is required for image enhancement. Histogram spreads or flattens the histogram of an image and due to this the pixels with lower intensity values appear darker and the pixels with higher intensity values appear lighter. So the contrast of the input image is improved. For human interpretation various techniques of image enhancement have been widely used in different applications areas of image processing as the subjective quality of images is mainly important

scholarly journals Image Enhancement Driven by Object Characteristics and Dense Feature Reuse Network for Ship Target Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1327
Author(s):  
Ling Tian ◽  
Yu Cao ◽  
Bokun He ◽  
Yifan Zhang ◽  
Chu He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
Target Detection ◽  
Semantic Information ◽  
Memory Storage ◽  
Generative Adversarial Network ◽  
Remote Sensing Imagery ◽  
Adversarial Network ◽  
High Level

As the application scenarios of remote sensing imagery (RSI) become richer, the task of ship detection from an overhead perspective is of great significance. Compared with traditional methods, the use of deep learning ideas has more prospects. However, the Convolutional Neural Network (CNN) has poor resistance to sample differences in detection tasks, and the huge differences in the image environment, background, and quality of RSIs affect the performance for target detection tasks; on the other hand, upsampling or pooling operations result in the loss of detailed information in the features, and the CNN with outstanding results are often accompanied by a high computation and a large amount of memory storage. Considering the characteristics of ship targets in RSIs, this study proposes a detection framework combining an image enhancement module with a dense feature reuse module: (1) drawing on the ideas of the generative adversarial network (GAN), we designed an image enhancement module driven by object characteristics, which improves the quality of the ship target in the images while augmenting the training set; (2) the intensive feature extraction module was designed to integrate low-level location information and high-level semantic information of different resolutions while minimizing the computation, which can improve the efficiency of feature reuse in the network; (3) we introduced the receptive field expansion module to obtain a wider range of deep semantic information and enhance the ability to extract features of targets were at different sizes. Experiments were carried out on two types of ship datasets, optical RSI and Synthetic Aperture Radar (SAR) images. The proposed framework was implemented on classic detection networks such as You Only Look Once (YOLO) and Mask-RCNN. The experimental results verify the effectiveness of the proposed method.

Fusion of Underwater Image Enhancement and Restoration

2019 ◽  
Vol 34 (03) ◽  
pp. 2054007 ◽  
Author(s):  
Rajni Sethi ◽  
Sreedevi Indu
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Laplacian Pyramid ◽  
Novel Technique ◽  
Underwater Image ◽  
Single Input ◽  
Color Cast ◽  
Better Than

Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.

scholarly journals Image Enhancement by Histogram Specification Using Multiple Target Images

2011 ◽  
pp. 29-37
Author(s):  
Pavithra P ◽  
Ramyashree N ◽  
Shruthi T.V ◽  
Dr. Jharna Majumdar
Keyword(s):  
High Resolution ◽  
Image Enhancement ◽  
Input Image ◽  
Multiple Target ◽  
Target Image ◽  
Image Histogram ◽  
Histogram Specification ◽  
High Resolution Images

Shape and characteristics of the histogram plays a major role in finding the quality of an image. Histogram Specification is an image enhancement technique, where the histogram of the input image is transformed to a pre-specified histogram derived from a high resolution image, called target image. In this paper, the classical histogram specification technique is extended by using a target image which is obtained by fusing multiple high resolution images. A set of Quality Metrics were identified to assess the quality of the output enhanced image. The paper addresses the following issues: a) Effect of varying the number of target images on the quality of the output enhanced image b) Role of using different methods of fusion on the quality of the output enhanced image c) Category of the target image on the quality of the output enhanced image. If the input image is from a forest, whether in order to obtain an enhanced image, all target images has to be selected from the forest category d) Effect of preprocessing of target image on the quality of the output enhanced image.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

2017 ◽  
Vol 1 (3) ◽  
pp. 54
Author(s):  
BOUKELLOUZ Wafa ◽  
MOUSSAOUI Abdelouahab
Keyword(s):  
Performance Metrics ◽  
Radiation Treatment ◽  
Hybrid Methods ◽  
Dose Calculation ◽  
Absolute Error ◽  
Ct Images ◽  
General Technique ◽  
Therapy Field ◽  
The Brain

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

scholarly journals New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

2021 ◽  
Vol 13 (5) ◽  
pp. 860
Author(s):  
Yi-Chun Lin ◽  
Tian Zhou ◽  
Taojun Wang ◽  
Melba Crawford ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Control Strategies ◽  
Target Function ◽  
Canopy Cover ◽  
Surface Model ◽  
Flowering Date ◽  
Sensing Platforms ◽  
Geolocation Accuracy ◽  
New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

Sign in / Sign up

Export Citation Format

Share Document