Radiometric Correction Method Research Articles

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

A General Relative Radiometric Correction Method for Vignetting Noise Drift

Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors’ noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method.

Solar radiation modeling and simulation of hyperspectral satellite data

In this research, we are interested in applying the model to simulate the radiative transfer through the atmosphere under realistic conditions for assessing the significance of the effects of the atmosphere and conditions on shooting satellite images. The main objective of this application is the analysis of satellite measurements, along with their variations atmospheric parameters. The purpose of modeling is to understand how different components of the measurement system combine to make a measurement. The form and content of a model depends on their purpose. The model is constructed to describe and characterize the measurement system to understand the phenomena which he is registered and to predict their behavior under the effect of an external action or as a result of a partial modification of the system itself same. The model developed is to break the middle-ground atmosphere into subsets in interaction with the solar spectrum and the sensor onboard the satellite. The proposed radiometric correction method is simple because it is based on pixels that are known to support their radiometric images. The luminances are simulated using the software (SDDS) which allows us to establish rules between reported digital luminance and luminance reflectance.

Analysis of shoreline changes on the northeast coast of Nunukan Island, Nunukan Regency, North Kalimantan Province

This study aimed to analyze changes in the coastline in the Northeast of Nunukan Island, North Kalimantan Province and to determine the area in the Northeast of Nunukan Island, North Kalimantan Province, which experienced changes in the coastline. This research was conducted in February - March 2022 on the Northeast Coast of Nunukan Island, North Kalimantan Province. The type of data collected is satellite imagery for the last 6 years, 2017-2022. Data were analyzed using GIS software with cropping, radiometric correction, geometric correction, image enhancement, shoreline digitization and overlay methods. The results showed that the rate of change of the eastern coastline of Nunukan Island in 2017 was 13,457.91 m long, 2018 was 14,266.82 m long, 2019 was 14,787.25 m long, 2020 was 14,762.21 m long, 2021 was 15,369.23 m long, and in 2022 it is 15,968.22 m. The area that has experienced a change in coastline on Nunukan Island, North Kalimantan Province, namely in 2017 covering an area of 21.70 ha, in 2018 covering an area of 22.27 ha, in 2019 covering an area of 23.34 ha, in 2020 covering an area of 23.74 ha, in 2021 covering an area of 24 .26 ha and in 2022 it will be 32.82 ha. This study concludes that the area of change in the coastline has changed significantly from 2017 to 2022.

3D compressive imaging system with a single photon-counting detector.

For photon-counting based compressive imaging systems, it is difficult to obtain 3D image with intensity and depth information precisely due to the dead time and shot noise effect of photon-counting detectors. In this study, we design and achieve a 3D compressive imaging system using a single photon-counting detector. To overcome the radiometric distortion arising from the dead time and shot noise, considering the response mechanism of photon-counting detectors, a Bayesian posterior model is derived and a Reversible jump Markov chain Monte Carlo (RJMCMC)-based method is proposed to iteratively obtain model parameters. Experimental and simulation results indicate that the 3D image of targets can be effectively and accurately reconstructed with a smaller number of repeated illuminations and no longer restricted by the photon flux conditions (i.e., breaking through the upper limit of the received signal level). The proposed Bayesian RJMCMC-based radiometric correction method is not only beneficial to single-photon 3D compressive imaging system, but also to any other photon-counting based systems, e.g., photon-counting lidars. In addition, limiting condition of recovering the actual photon number for photon-counting imaging or lidar systems is also quantitatively analyzed, which is of great significance to the system scheme design.

Comprehensive evaluation of landscape architecture environment quality based on multi-source remote sensing technology

In order to overcome the problems of low accuracy of information collection and large error of comprehensive evaluation of environmental quality in traditional methods, a comprehensive evaluation of landscape architecture environment quality based on multi-source remote sensing technology was proposed. Multi-source remote sensing images were collected, and the remote sensing images were registered by radiometric correction and geometric correction methods, and environmental information of landscape architecture was collected. According to the collected information, the comprehensive evaluation index system of landscape environment quality was established. The weight distribution of each evaluation index was determined by fuzzy comprehensive evaluation model and single index membership function. The comprehensive evaluation model of landscape environment quality was built according to the evaluation index weight to achieve the comprehensive evaluation of environmental quality. Experimental results show that the accuracy of this method varies from 93% to 97%, and the average evaluation error is 3.51.

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

Radiometric correction is one of the most important preprocessing parts of unmanned aerial vehicle (UAV) multispectral remote sensing data analysis and application. In this article, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. First, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Second, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was five (digital number, UAV sensor height, wavelength, solar altitude angle, and temperature) and the number of nodes in the hidden layer was eight, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and cross-validation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method and radiative transfer model. In general, the research results of this article prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.

IMPROVING LOCAL ADAPTIVE FILTERING METHOD EMPLOYED IN RADIOMETRIC CORRECTION OF ANALOGUE AIRBORNE CAMPAIGNS

Abstract. An orthophotomosaic is as a single image that can be layered on a map. It is produced from a set of aerial images impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as the camera itself, like lens vignetting. These create some unsightly radiometric inhomogeneity in the mosaic that could be corrected by using a local adaptive filter, also named Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This current work introduces two elementary studies. In a first time, in order to quantify the loss of contrast due to the use of Wallis filter, a simple multi-scale score is proposed based on mathematical morphology operations. In a second time, an optimal window size for the filter is identified by considering some systematic radiometric behaviours in the images forming the mosaic through Principal Component Analysis (PCA). These two elementary studies are preliminary steps leading to a method of radiometric correction combining Wallis filtering and PCA.

Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery

Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.

The use of the short-lived radioisotope <sup>188</sup>Re in the analytical practice of rhenium determination

The possibility of using the short-lived radioisotope 188Re (16.9 h) in the radioactive-tracer technique for estimation of the losses occurred during sample preparation and for analysis of the solutions with a low rhenium content is shown. The radioisotope was obtained in an 188Re generator, which is a glass column filled with aluminum oxide with a pre-adsorbed parent isotope 188W (69.4 days) that forms 188Re during β-decay. The latter in the form of a perrhenate ion was selectively washed into an aqueous solution, which was used to obtain a labeled solution. The values of rhenium losses and their distribution over the stages of sample preparation of carbon-containing raw materials (e.g., high-viscosity oil) including sintering, leaching, filtration and evaporation are estimated. It is shown that the greatest amount of rhenium is lost at the stage of sintering. However, the use of MgO + KMnO4 mixture minimize the losses occurred at this stage and, hence, the total losses of rhenium. A rapid procedure of rhenium determination using the method of radiometric correction with substoichiometric separation is described. The method is based on the quantitative extraction of a stable complex of tetraphenylphosphonium cation (TPhPh) with the perrhenate ion by dichloroethane. The extraction constant of (C6H5)4PReO4 complex is (3.03 ± 0.75) × 106. The extraction of the perrhenate ion not complexing with TPhPh does not exceed 1 – 2 %. The developed method provides rhenium determination in the sample at a level of 1 – 100 μg. A 10-fold excess of Zn2+ and Ni2+ present in a chloride-containing medium affects the determination of rhenium. The anions , , and have the most pronounced interfering effect whereas , , and Cl– do not interfere with the determination.

Anisotropic Total Variation Regularized Low-Rank Approximation for SSS Images Radiometric Distortion Correction

Radiometric distortion caused by the time-varying gain (TVG), beam patterns, angular responses, and sonar altitude variations, highly degrades the quality of side-scan sonar (SSS) images. Thus, radiometric distortion correction becomes a fundamental step for SSS image processing which holds vital importance for geomorphic applications. However, existing methods cannot take the prior information of the acoustic illumination component as well as the feature of seafloor into consideration well, which would easily cause damage to the image and also always be powerless for residual stripe noise. In this paper, a novel radiometric correction method is proposed. First, we give a detailed analysis of the SSS imaging theory based on the Lambert’s law as well as the prior knowledge about the characteristics of SSS images. Then, incorporating the prior of the SSS imaging process, the low-rank constraint is specifically introduced for the illumination component, while the anisotropic total variation (ATV) constraint is used to constraint the albedo component, combining other constraints, a decomposition model is proposed to correct the radiometric distortion based on the SSS imaging theory. And an alternative minimization method has been adopted to solve the proposed model effectively. Experiments proved the validity of the proposed method.

A General Relative Radiometric Correction Method for Vignetting and Chromatic Aberration of Multiple CCDs: Take the Chinese Series of Gaofen Satellite Level-0 Images for Example

The relative radiometric correction for Level-0 images captured by spaceborne push-broom imaging system faces the problems of vignetting, chromatic aberration, brightness saturation difference, misalignment, and so on. This article proposed a general relative radiometric correction method, which was applied to Gaofen series satellite Level-0 images. In the course of vignetting calibration, the proposed method based on the gray-level co-occurrence matrix (GLCM) did not use side-slither data and the DN_MAX truncation method can solve brightness saturation difference. During chromatic aberration calibration, the subpixel-based phase correlation algorithm was first used to register adjacent CCDs, and then, the proposed global optimization method was adapted to calibrate multiple CCDs. The fixed calibration parameters for vignetting and chromatic aberration calculated by ridge regression and Newton’s method can be directly applied to correct other orbital Level-0 images. To verify the robustness and universality, Level-0 images of GF-1B, GF-1C, GF-1D, and GF-2 satellites were chosen for experiments, and the results were better than the existing methods. In addition, some official Level-1 products of GF-1B and GF-1C, covering particularly dark or bright surfaces (e.g., snow, sea, and cloud), were used to compare with the calibrated images of the proposed method. Results showed that relative radiometric correction by applying the independently estimated calibration parameters in this work achieved satisfactory results without the defects existing in official Level-1 products. Finally, results of relative radiometric correction of 30 orbital Level-0 images around the world further strengthened the conclusion that the estimated calibration parameters can be reused in other regions or seasons.

MACA: A Relative Radiometric Correction Method for Multiflight Unmanned Aerial Vehicle Images Based on Concurrent Satellite Imagery

Unmanned aerial vehicle (UAV) offers an unprecedented observing potential with ultrahigh spatial and temporal resolutions and high flexibility. However, it remains difficult to solve the radiometric inconsistency of multiflight UAV imagery. This study proposed a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery (MACA) consisting of two steps, i.e., cross-sensor spectral fitting (CSF) and fine-resolution spectral calibration (FSC). In CSF, relationships of multiflight UAV reflectance and the concurrent satellite reflectance were established for generating the fine-resolution reference imagery. Subsequently, a relative radiometric correction model was constructed in the FSC step to correct multiflight UAV imagery. The performance of MACA was evaluated using multiflight UAV datasets acquired on six cropland sites and a concurrent Sentinel-2 image. Compared with four typical or state-of-the-art relative correction methods, the correction using MACA yielded better consistency between UAV and Sentinel-2 data, regardless of individual spectral bands (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.79$ </tex-math></inline-formula>&#x2013;0.86, root mean square error (RMSE) <inline-formula> <tex-math notation="LaTeX">$=0.004$ </tex-math></inline-formula>&#x2013;0.019) or vegetation indices (VIs) (<inline-formula> <tex-math notation="LaTeX">$\text{R}^{2} =0.80$ </tex-math></inline-formula>&#x2013;0.86, RMSE <inline-formula> <tex-math notation="LaTeX">$=0.024$ </tex-math></inline-formula>&#x2013;0.054). Moreover, the prediction of plant nitrogen accumulation (PNA) based on the MACA-corrected UAV data had the highest accuracy and showed the spatial variation most significantly within and between fields for all sites. The results demonstrated that MACA was more robust in reducing spectral mismatch across sensors and eliminating the subjective error of pseudo-invariant features (PIFs) selection. MACA has the potential to be used to cross-calibrate multisensor data into a consistent standard, which will benefit multisensor synergies.

Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

Optical remote sensing has been suggested as a preferred method for monitoring submerged aquatic vegetation (SAV), a critical component of freshwater ecosystems that is facing increasing pressures due to climate change and human disturbance. However, due to the limited prior application of remote sensing to mapping freshwater vegetation, major foundational knowledge gaps remain, specifically in terms of the specificity of the targets and the scales at which they can be monitored. The spectral separability of SAV from the St. Lawrence River, Ontario, Canada, was therefore examined at the leaf level (i.e., spectroradiometer) as well as at coarser spectral resolutions simulating airborne and satellite sensors commonly used in the SAV mapping literature. On a Leave-one-out Nearest Neighbor criterion (LNN) scale of values from 0 (inseparable) to 1 (entirely separable), an LNN criterion value between 0.82 (separating amongst all species) and 1 (separating between vegetation and non-vegetation) was achieved for samples collected in the peak-growing season from the leaf level spectroradiometer data. In contrast, samples from the late-growing season and those resampled to coarser spectral resolutions were less separable (e.g., inter-specific LNN reduction of 0.25 in late-growing season samples as compared to the peak-growing season, and of 0.28 after resampling to the spectral response of Landsat TM5). The same SAV species were also mapped from actual airborne hyperspectral imagery using target detection analyses to illustrate how theoretical fine-scale separability translates to anin situ, moderate-spatial scale application. Novel radiometric correction, georeferencing, and water column compensation methods were applied to optimize the imagery analyzed. The SAV was generally well detected (overall recall of 88% and 94% detecting individual vegetation classes and vegetation/non-vegetation, respectively). In comparison, underwater photographs manually interpreted by a group of experts (i.e., a conventional SAV survey method) tended to be more effective than target detection at identifying individual classes, though responses varied substantially. These findings demonstrated that hyperspectral remote sensing is a viable alternative to conventional methods for identifying SAV at the leaf level and for monitoring at larger spatial scales of interest to ecosystem managers and aquatic researchers.

AUTOMATED REFLECTANCE TARGET DETECTION FOR AUTOMATED VICARIOUS RADIOMETRIC CORRECTION OF 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.

Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.

Processing and analysis of airborne full-waveform laser scanning data for the characterization of forest structure and fuel properties

&lt;p class="Bodytext"&gt;This PhD thesis addresses the development of full-waveform airborne laser scanning (ALS&lt;sub&gt;FW&lt;/sub&gt;) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALS&lt;sub&gt;FW&lt;/sub&gt; metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALS&lt;sub&gt;FW&lt;/sub&gt; data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALS&lt;sub&gt;FW&lt;/sub&gt;, discrete airborne laser scanning (ALS&lt;sub&gt;D&lt;/sub&gt;) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALS&lt;sub&gt;FW&lt;/sub&gt; data, as well as characterized by using the new proposed metrics.&lt;/p&gt;

A simple model for PIFs extraction at digital change detection approach

The radiometric normalization using pseudo-invariant features (PIFs) is one of the best methods of image-based atmospheric correction. However, one of the main challenges of this approach is the correct selection of PIFs. So far many efforts have been made to automate radiometric correction using PIFs, although the capability of all these automated methods has been proven in various studies, their application requires intermediate to advanced statistical and computational knowledge and their application is beyond the reach of most conventional remote sensing users. Therefore, in this research, a straightforward simple method is proposed based on the definition of PIFs that automatically identifies these areas and uses them on a regression procedure for automatic normalization. The study area is Hara Protection Area (the Hara international wetland), which has sub-tropical climate and due to surface homogeneity of its mangrove forests can be used as a pilot for natural, and homogeneous ecosystems such as forested areas. To create an automatic PIFs extraction in this area, initially, we masked areas that should not be in the PIFs by applying some thresholds. At this stage, water, vegetation and mountainous areas were filtered. Then, the radiometric resolution of the two different sensor images was identical, and using the differential histogram shape of the two images, unvaried areas were extracted. To validate the accuracy of the proposed method, absolute radiometric correction using atmospheric and topographic correction (ATCOR), fast line-of-sight atmospheric analysis of hypercubes (FLAASH) and atmospheric correction (ATMOSC) methods, and relative radiometric correction using both empirical line calibration method and dark object subtraction method and automatic radiometric correction using QUick atmospheric correction (QUAC) and autonomous atmospheric correction (IMAGINE AAIC) were applied on the data. The output of all atmospheric correction methods and the proposed method was applied in the image algebra change detection in the form of a difference and with a threshold of twice the standard deviation from the mean to be checked by 219 points. The results of validation and qualitative studies of the histogram comparisons proved the proper functioning of the proposed method (κ greater than 0.8), and using cross tables, the performance of the proposed method is similar to that of the empirical line calibration method (more than 76%). Finally, a few unique features in the current research proposal, including simplicity, automation, negligible systematic error, the possibility of using in a biomarker degradation warning system, the independence on the type of sensor used, distinguish it from other radiometric correction methods.

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

Improved relative radiometric normalization method of remote sensing images for change detection

Relative radiometric normalization (RRN) of remotely sensed images is often a preprocessing step during time series analysis and change detection. Conventional RRN methods may lessen the radiation difference of changed pixels in images during the RRN process, thus reducing the accuracy of change detection. To solve this problem, we propose a relative radiometric correction method based on wavelet transform and iteratively reweighted multivariate alteration detection (IR-MAD). A wavelet transform is applied to separate high and low frequency components of both the target image and reference image. The high frequency components remain unprocessed to preserve high frequency information. We use the IR-MAD algorithm to normalize the low frequency component of the target image. A reverse wavelet transform reconstructs the radiometrically normalized image. We tested the proposed method with traditional histogram matching, mean variance, the original IR-MAD method, and a method combining wavelet transform and low-pass filtering, and change detection was conducted to evaluate the RRN quality. The experiments show that the proposed method can not only effectively eliminate the overall radiation difference between images but also enable higher accuracy of change detection.