scholarly journals AUTOMATED REFLECTANCE TARGET DETECTION FOR AUTOMATED VICARIOUS RADIOMETRIC CORRECTION OF UAV IMAGES

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 133-137
Author(s):  
S. Ban ◽  
T. Kim
Keyword(s):  
Target Detection ◽  
Spectral Reflectance ◽  
Detection Method ◽  
Correction Method ◽  
Radiometric Calibration ◽  
Radiometric Correction ◽  
Vicarious Calibration ◽  
Automatic Target Detection ◽  
Aerial Vehicle ◽  
Uav Images

Abstract. Recently, with increasing use of unmanned aerial vehicle (UAV), radiometric calibration of UAV images has become an important pre-processing step for application such as vegetation mapping, crop field monitoring, etc. In order to obtain accurate spectral reflectance, some UAVs measure irradiance at the time of image acquisition. However, most of UAV systems do not have such irradiance sensors. In these cases, vicarious radiometric correction method has to be used. Digital numbers (DNs) of imaged ground reflectance targets are measured and spectral reflectance is acquired from with known reflectance values of the targets. For automated vicarious calibration, a technique for automatically detecting image location of ground reflectance targets has been developed. In this study, we report an improved version of automated reflectance target detection and a new semi-automatic reflectance target detection developed. Test results showed that among the 14 reflectance targets, 13 targets were detected with the automatic target detection method. The undetected target was extracted by the proposed semi-automatic target detect method. Additional test was conducted on the remaining targets to confirm the applicability of our semi-automatic target detection method. As a result, other targets were also detected. The proposed automated and semi-automated target detection method can be used for automated vicarious calibration of UAV images.

scholarly journals Terrain-Based Shadow Correction Method for Assessing Supraglacial Features on the Greenland Ice Sheet

2021 ◽  
Vol 2 ◽  
Author(s):  
Sasha. Z. Leidman ◽  
Åsa K. Rennermalm ◽  
Richard G. Lathrop ◽  
Matthew. G. Cooper
Keyword(s):  
Land Surface ◽  
Greenland Ice Sheet ◽  
Correction Method ◽  
Correction Coefficient ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Sediment Cover ◽  
Elevation Model ◽  
Uav Images ◽  
Gis Software

The presence of shadows in remotely sensed images can reduce the accuracy of land surface classifications. Commonly used methods for removing shadows often use multi-spectral image analysis techniques that perform poorly for dark objects, complex geometric models, or shaded relief methods that do not account for shadows cast on adjacent terrain. Here we present a new method of removing topographic shadows using readily available GIS software. The method corrects for cast shadows, reduces the amount of over-correction, and can be performed on imagery of any spectral resolution. We demonstrate this method using imagery collected with an uncrewed aerial vehicle (UAV) over a supraglacial stream catchment in southwest Greenland. The structure-from-motion digital elevation model showed highly variable topography resulting in substantial shadowing and variable reflectance values for similar surface types. The distribution of bare ice, sediment, and water within the catchment was determined using a supervised classification scheme applied to the corrected and original UAV images. The correction resulted in an insignificant change in overall classification accuracy, however, visual inspection showed that the corrected classification more closely followed the expected distribution of classes indicating that shadow correction can aid in identification of glaciological features hidden within shadowed regions. Shadow correction also caused a substantial decrease in the areal coverage of dark sediment. Sediment cover was highly dependent on the degree of shadow correction (k coefficient), yet, for a correction coefficient optimized to maximize shadow brightness without over-exposing illuminated surfaces, terrain correction resulted in a 49% decrease in the area covered by sediment and a 29% increase in the area covered by water. Shadow correction therefore reduces the overestimation of the dark surface coverage due to shadowing and is a useful tool for investigating supraglacial processes and land cover change over a wide variety of complex terrain.

scholarly journals INCORRECT MATCH DETECTION METHOD FOR ARCTIC SEA-ICE RECONSTRUCTION USING UAV IMAGES

2018 ◽  
Vol XLII-2 ◽  
pp. 501-505 ◽  
Author(s):  
J.-I. Kim ◽  
H.-C. Kim
Keyword(s):  
Sea Ice ◽  
Detection Method ◽  
Arctic Sea Ice ◽  
Surface Model ◽  
Target Area ◽  
Incorrect Match ◽  
Arctic Sea ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Key Indicators

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

A fast radiometric correction method for Sentinel-2 satellite images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Elahe Moradi ◽  
Alireza Sharifi
Keyword(s):  
Polynomial Equation ◽  
Correction Method ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Radiometric Correction ◽  
Content Type ◽  
Spectral Bands ◽  
Corrected Data ◽  
Sentinel 2A

Purpose Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration. Design/methodology/approach The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation. Findings This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable. Originality/value For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

scholarly journals UAV-BASED 3D URBAN ENVIRONMENT MONITORING

2018 ◽  
Vol IV-3 ◽  
pp. 37-43
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Huaqing Liu ◽  
Binbin Zhao ◽  
Lingfeng He
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Three Dimensions ◽  
Surface Model ◽  
Environment Monitoring ◽  
Great Flexibility ◽  
Accuracy And Precision ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Uav Images

Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.

scholarly journals Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1726 ◽  
Author(s):  
Jung-Il Shin ◽  
Yeong-Min Cho ◽  
Pyung-Chae Lim ◽  
Hae-Min Lee ◽  
Ho-Yong Ahn ◽  
...  
Keyword(s):  
Optimal Path ◽  
Calibration Method ◽  
Path Selection ◽  
Radiometric Calibration ◽  
Validation Data ◽  
Geometric Calibration ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reflectance Map ◽  
Relative Radiometric Calibration

As the use of unmanned aerial vehicle (UAV) images rapidly increases so does the need for precise radiometric calibration. For UAV images, relative radiometric calibration is required in addition to the traditional vicarious radiometric calibration due to the small field of view. For relative radiometric calibration, some UAVs install irradiance sensors, but most do not. For UAVs without them, an intelligent scheme for relative radiometric calibration must be applied. In this study, a relative radiometric calibration method is proposed to improve the quality of a reflectance map without irradiance measurements. The proposed method, termed relative calibration by the optimal path (RCOP), uses tie points acquired during geometric calibration to define the optimal paths. A calibrated image from RCOP was compared to validation data calibrated with irradiance measurements. As a result, the RCOP method produces seamless mosaicked images with uniform brightness and reflectance patterns. Therefore, the proposed method can be used as a precise relative radiometric calibration method for UAV images.

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