| 471. |
- Dibs, Hayder, et al.
(författare)
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Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images
- 2020
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Ingår i: Journal of Civil Engineering and Architecture. - USA : David Publishing Company. - 1934-7359 .- 1934-7367. ; 14:6, s. 339-350
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Tidskriftsartikel (refereegranskat)abstract
- The scale-invariant feature transform (SIFT) ability to automatic control points (CPs) extraction is very well known on remote sensing images, however, its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs, their matching has high false alarm. This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference (SAD) in different manner for the new optical satellite generation called near-equatorial orbit satellite (NEqO) and multi-sensor images. The proposed method leads to improving CPs matching with a significantly higher rate of correct matches. The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area. The proposed method consists of three parts: (1) applying the SIFT to extract CPs automatically, (2) refining CPs matching by SAD algorithm with empirical threshold, and (3) evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method. The result indicates an accurate and precise performance of the model, which showed the effectiveness and robustness of the proposed approach.
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| 472. |
- Dibs, Hayder, et al.
(författare)
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Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images
- 2022
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Ingår i: Innovative Infrastructure Solutions. - : Springer. - 2364-4176 .- 2364-4184. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.
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| 473. |
- Dibs, Hayder, et al.
(författare)
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Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing
- 2023
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Ingår i: Emerging Science Journal. - : Ital Publication. - 2610-9182. ; 7:2, s. 428-444
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Tidskriftsartikel (refereegranskat)abstract
- Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone.
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| 474. |
- Dibs, Hayder, et al.
(författare)
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Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis
- 2021
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Ingår i: Geotechnical and Geological Engineering. - : Springer. - 0960-3182 .- 1573-1529. ; 39:8, s. 5825-5842
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Tidskriftsartikel (refereegranskat)abstract
- Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.
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| 475. |
- Dibs, Hayder, et al.
(författare)
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Geometric Correction Analysis of Highly Distortion of Near Equatorial Satellite Images Using Remote Sensing and Digital Image Processing Techniques
- 2022
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Ingår i: Engineering. - : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 14:01, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- The Near-equatorial orbit (NEqO) satellite represents a new generation of optical satellite images characterized by nonlinear distortion when captured. Conventional modeling techniques are insufficient to overcome the geometric distortion in these satellite images. This study proposes a new methodology for overcoming the geometric distortion of the NEqO images. The data used are obtained from RazakSAT and SPOT-5 satellite images in Malaysia. The method starts with applying the RI-SIFT algorithm to extract control points (CPs) automatically. These CPs are used to solve for the transformation parameters of the geometric correction model by applying spline transformations. The result is verified through statistical comparison: 1) geometric correction on the RazakSAT image is performed with Spot satellite image with using first-order polynomial trans-formation. 2) Then calculate the root mean square error (RMSE). 3) Compare the calculated RMSE with that obtained from the first step with that of the proposed method. The RMSE value of the geometric corrections using the proposed method was 7.08 × 10−9 m. The proposed method provides promising results.
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| 476. |
- Dibs, Hayder, et al.
(författare)
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Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial Algorithms and Architecture Environment
- 2023
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Ingår i: Engineering. - : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 15:4, s. 220-233
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Tidskriftsartikel (refereegranskat)abstract
- The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.
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| 477. |
- Dibs, Hayder, et al.
(författare)
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Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis
- 2023
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Ingår i: Emerging Science Journal. - : Ital Publication. - 2610-9182. ; 7:4, s. 1215-1231
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Tidskriftsartikel (refereegranskat)abstract
- Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView 2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this.
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| 478. |
- Dibs, Hayder, et al.
(författare)
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Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis
- 2023
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Ingår i: Engineering. - : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 15:2, s. 75-89
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Tidskriftsartikel (refereegranskat)abstract
- Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.
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| 479. |
- Dibs, Hayder, et al.
(författare)
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Simulate New Near Equatorial Satellite System by a Novel Multi-Fields and Purposes Remote Sensing Goniometer
- 2020
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Ingår i: Engineering. - USA : Scientific Research Publishing. - 1947-3931 .- 1947-394X. ; 12:6, s. 325-346
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Tidskriftsartikel (refereegranskat)abstract
- Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system called near-equatorial satellite system to perform different kinds of remote sensing applications in equatorial regions. This study proposed a newly invented laboratory and fieldwork goniometer designed to simulate and capture intensity variation and measure the bidirectional spectral reflectance of earth surface. The proposed goniometer is a multi-purpose and multi-field device. It is able to simulate different satellite systems and measure the intensity variation and spectral reflectance of earth’s surface features with freely azimuth and zenith angles of sensors and illumination source in fieldwork and/or laboratory. However, the system of invention is focusing on specific satellite orbital to work with the parameters and properties of NEqO satellite system in order to obtain NEqO system imagery for performing different applications such as geometric correction, relative radiometric normalization and change detection for future work. The significant of this invention is that most of the invented goniometers of remote sensing are able to work just in field or just in laboratory and use, carry just optical sensor or hyperspectral sensor. Specifically, our invention can do all these functions that are not available in existing goniometers. The proposed device offers several advantages, namely, high measurement speed, flexibility, low cost, efficiency, and possible measurement depending on the free zenith/azimuth angles of sensors and illumination sources. The proposed goniometer includes ten parts, and two different sensors (optical and hyperspectral).
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| 480. |
- Dimple, Dimple, et al.
(författare)
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Determining the Hydrological Behaviour of Catchment Based on Quantitative Morphometric Analysis in the Hard Rock Area of Nand Samand Catchment, Rajasthan, India
- 2022
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Ingår i: Hydrology. - : MDPI. - 2306-5338. ; 9:2
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Tidskriftsartikel (refereegranskat)abstract
- India’s water resources are under tremendous pressure due to elevated demand for various purposes. The over-exploitation of these valuable resources has resulted in an imbalance in the watershed ecology. The application of spatial analysis tools in studying the morphological behaviour of watersheds has increased in recent decades worldwide due to the accessibility of the geospatial database. A morphometric analysis of a river basin is vital to determine the hydrological behaviour to develop effective management. Under the current study, morphological behaviour of Nand Samand catchment in the hard rock region was evaluated employing remote sensing (RS) and geographical information system (GIS) tools. The Nand Samand catchment (Rajasthan State, India) has an area of 865.18 km2 with the highest and lowest elevations of 1318 m and 570 m above mean sea level, respectively. This study utilises a 30 m high-spatial-resolution ASTER imagery digital elevation model for delineating the catchment. The drainage network is assessed using a GIS method, and morphometric parameters like linear, areal, and relief aspects were calculated. Results were obtained for parameters viz., basin length of 82.66 km, constant channel maintenance equal to 0.68 km, stream frequency of 2.11 km−2, drainage density of 1.48 km−1, and length overflow of 0.34 km. Form factor of 0.13, and the circulatory ratio of 0.28 showed that an elongated shape characterises the study area. The results would help understand the relationship between hydrological variables and geomorphological parameters for better decision-making. The techniques used could effectively help to perform better drainage basin and channel network morphometric analyses. The found morphometric characteristics will be helpful in understanding the Nand Samand catchment and similar areas in India in order to better guide the decision-makers in providing adequate policy to the development of the region
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