Radiometric Inconsistency Research Articles

Assessing the spatial resolution distance of satellite images: SuperDove versus Landsat 8

ABSTRACT The remote sensing community usually confuses the Spatial Resolution Distance (SRD) of satellite images with their Ground Sampling Distance (GSD). This misconception has been highlighted by assessments of Planet’s CubeSat images conducted independently by NASA and ESA, which found that their SRD is about five times larger than their GSD. The discrepancies between different metrics to compute the SRD have contributed to the confusion between SRD and GSD. A recently developed spatial resolution metric computed in terms of the imaging sensor’s Point Spread Function (PSF), named the Spatial Resolution Function (SRF), allows a comprehensive assessment of the SRD as a function of the resolving contrast generated in the image, when there are two-point sources in the scene. The SRF shows under what conditions the GSD is a good approximation to the SRD and provides a tool to assess the predictions of other metrics used to compute the SRD. We use the SRF to compute the SRD of images captured by Landsat 8 and Planet’s SuperDove satellites. The PSF of these sensors is calculated by using the specifications and measurements provided by their operators. The SRD predictions of the SRF metric agree with NASA and ESA findings, showing that the average SRD of SuperDove and Landsat 8 images is 4.9 and 1.4 times larger than their GSD, respectively. The stability of the SRD of these images is assessed by computing the degradation (increase) of SRD resulting from an increase of atmospheric turbulence and spacecraft vibrations. We conclude that the confusion between SRD and GSD has led to the development of CubeSat images with a small GSD whose SRD is several times larger than their GSD. These CubeSat images have an intrinsically unstable SRD in the space environment, a property that explains the numerous radiometric inconsistencies reported by their users.

Relaxation-Based Radiometric Normalization for Multitemporal Cross-Sensor Satellite Images

Multitemporal cross-sensor imagery is fundamental for the monitoring of the Earth’s surface over time. However, these data often lack visual consistency because of variations in the atmospheric and surface conditions, making it challenging to compare and analyze images. Various image-normalization methods have been proposed to address this issue, such as histogram matching and linear regression using iteratively reweighted multivariate alteration detection (IR-MAD). However, these methods have limitations in their ability to maintain important features and their requirement of reference images, which may not be available or may not adequately represent the target images. To overcome these limitations, a relaxation-based algorithm for satellite-image normalization is proposed. The algorithm iteratively adjusts the radiometric values of images by updating the normalization parameters (slope (α) and intercept (β)) until a desired level of consistency is reached. This method was tested on multitemporal cross-sensor-image datasets and showed significant improvements in radiometric consistency compared to other methods. The proposed relaxation algorithm outperformed IR-MAD and the original images in reducing radiometric inconsistencies, maintaining important features, and improving the accuracy (MAE = 2.3; RMSE = 2.8) and consistency of the surface-reflectance values (R2 = 87.56%; Euclidean distance = 2.11; spectral angle mapper = 12.60).

Radiometric Principle-Based Radiometric Normalization Method for SAR Images Mosaic

Radiometric normalization minimizes the radiometric inconsistencies between images in synthetic aperture radar (SAR) images mosaic. However, the radiometric principle of SAR have not been fully considered by the existing methods. To this issue, a radiometric principle-based radiometric normalization method is proposed. First, image areas with consistent coverage areas in object space and approximate rough ground surface between images are extracted as adjustment candidates based on imaging principle. Then, considering the radiometric characteristic of SAR image, all images are taken as a whole to solve the radiometric adjustment model, which transfers radiometric normalization into the least square optimization. Finally, a global quantization strategy is used to ensure radiometric consistency during orthorectification and mosaicking. The experimental results of Gaofen-3 SAR images demonstrate that the proposed method has the best performance, and can effectively and stably eliminate the radiometric differences between images.

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.

LOESS radiometric correction for contiguous scenes (LORACCS): Improving the consistency of radiometry in high-resolution satellite image mosaics

Analyses of Planet Dove (CubeSat) satellite imagery can prove challenging when radiometric consistency among contiguous scenes is necessary to apply algorithms across mosaics or through time. While temporal resolution is excellent due to the high number of Dove satellites, the radiometric consistency between scenes can be a limiting factor in the utility of the imagery when creating mosaics. To date, methods of addressing the radiometric consistency problem have focused on radiometric correction in a time-series stack, but do not address how to achieve seamless mosaics within that stack. We demonstrate a method to correct Planet Dove radiometric inconsistencies in mosaics using locally estimated scatterplot smoothing (LOESS). LOESS is a non-parametric regression technique that is more flexible to fitting a line to data than ordinary least squares regression (OLS), and better conforms to the nonlinearities in spectral responses among Dove sensors across brightness levels. A straightforward application of LOESS uses overlap areas between contiguous scenes, enabling correction independent of outside data sources. We demonstrate the effectiveness of the method to improve the quality of mosaics by reducing spectral discontinuities between overlapping images in multiple locations across multiple biome and landcover types. Further, the LOESS Radiometric Correction for Contiguous Scenes, or LORACCS, is able to be integrated with methods previously developed to normalize Dove imagery with sensors such as Landsat or Sentinel-2, typically for use in time series analysis. Written in Python, this method offers an open-source solution to a significant challenge when working with Dove data.

Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction

Dozens of spatiotemporal fusion methods have been developed to reconstruct vegetation index time-series data with both high spatial resolution and frequent coverage for monitoring land surface dynamics. Although several studies comparing the different fusion methods have been conducted, selecting the suitable fusion methods is still challenging, as inevitable influential factors tend to be neglected. To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. The results indicate that Fit-FC achieved the best performance with the strongest tolerance to geometric misregistration when radiometric inconsistency was negligible; thus, it is the first recommended algorithm for blending normalized difference vegetation index (NDVI) imagery. Instead, the FSDAF could generate the best results if radiometric inconsistency was non-negligible. These findings could help users determine the method that is appropriate for different remote sensing datasets, and provide guidelines for developers in the future development of novel methods.

Clouds and the Earth’s Radiant Energy System strategy for intercalibrating the new-generation geostationary visible imagers

The advanced baseline imager (ABI) instrument onboard Geostationary Operational Environmental Satellite (GOES)-16 is the first of National Oceanic and Atmospheric Administration (NOAA’s) new-generation geostationary earth orbiting (GEO) imagers that provides high-quality calibrated and geolocated Earth observations in six reflective solar bands (RSBs). The spectral similarity between the Visible Infrared Imaging Radiometer Suite (VIIRS) and ABI RSB offers an opportunity for deriving VIIRS-quality cloud retrievals from the ABI radiances. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project utilizes GEO imager (including ABI) radiances to retrieve clouds and derive broadband fluxes that are used to account for the regional diurnal flux variation between the CERES measurements and to convert the CERES observed radiances into fluxes. In order to derive a seamless cloud and flux datasets for CERES, it is important that the GEO, MODIS, and VIIRS imagers are all placed on the same radiometric scale. We describe an absolute radiometric intercomparison between the NOAA-20 VIIRS and GOES-16 ABI RSB using ray-matched radiance/reflectance pairs over all-sky tropical ocean scenes as well as a deep convective cloud invariant target calibration algorithm. Results indicate that the ABI and VIIRS RSB calibration are within 5%, except for the 0.47-μm band, for which the radiometric inconsistency is found to be ∼7 % . The GOES-16 radiometric scaling factors referenced to NOAA-20 VIIRS were computed from the two independent calibration methods to agree within 1% for ABI bands 1 to 4, and within 3% for bands 5 and 6. Results from this study were used to propose a future CERES GEO intercalibration algorithm referenced to NOAA-20 VIIRS, given the eventual demise of the Terra and Aqua satellites.

PlanetScope Radiometric Normalization and Sentinel-2 Super-Resolution (2.5 m): A Straightforward Spectral-Spatial Fusion of Multi-Satellite Multi-Sensor Images Using Residual Convolutional Neural Networks

Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of–the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.

Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)

ABSTRACT Radiometric normalization, as an essential step for multi-source and multi-temporal data processing, has received critical attention. Relative Radiometric Normalization (RRN) method has been primarily used for eliminating the radiometric inconsistency. The radiometric transforming relation between the subject image and the reference image is an essential aspect of RRN. Aimed at accurate radiometric transforming relation modeling, the learning-based non-linear regression method, Support Vector machine Regression (SVR) is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN. To evaluate the effectiveness of the proposed method, a series of experiments are performed, including two synthetic data experiments and one real data experiment. And the proposed method is compared with other methods that use linear regression, Artificial Neural Network (ANN) or Random Forest (RF) for radiometric transforming relation modeling. The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.

Improving phenological monitoring of winter wheat by considering sensor spectral response in spatiotemporal image fusion

Multisensor image fusion results may deviate from accurately reflecting the phenological stages of winter wheat because different responses of satellite sensors to the spectrum lead to the radiometric inconsistency between different remote sensing images. To reduce the effect of the difference in the physical electromagnetic spectrum responses between sensors on monitoring the phenological stages of winter wheat by fusion results, Sensor Spectral Response (SSR) should be considered in spatiotemporal fusion methods. This paper proposes a novel image fusion model by introducing SSR into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The contribution of SSR in minimizing the effect of the system difference between sensors on image fusion products is parameterized as a calibration factor by matrixing operation, which is able to offset the systematic inconsistency between different sensor images. Linear regression equation for different land cover type and spectral band is established to calculate the weights needed in STARFM for improving the selection of neighboring spectrally similar pixels. This proposed method is evaluated using one satellite datasets including four ZY-3 (5.8 m) and Landsat 8 OLI (30 m) scenes which are acquired during the growth stages of winter wheat from seedling to harvest. Qualitative and quantitative evaluation shows that the proposed method can better monitor the phenology of winter wheat with an improved spatial and temporal consistency with the observations than STARFM.

Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies

Distributed systems of small satellites are generating a new type of remote sensing data: multi-spectral data with high spatial and temporal resolution and near-daily global coverage. This data is proving to be valuable for monitoring land cover change. However, in order to achieve widespread application for this purpose, all satellites in the distributed system, or constellation, must acquire imagery with accurate georeferencing and consistent radiometric properties. In this research, we developed a method to automatically co-register and radiometrically normalize a temporally dense series of smallsat images using geocorrected reference imagery. To demonstrate the approach, we normalized a smallsat image time series at both the pixel and the polygon level, smoothing spectral indices through time to detect both abrupt and gradual changes. Using PlanetScope imagery, we tested these methods in a forested region of British Columbia, Canada heavily impacted by forest fires in 2017. By examining the normalized difference vegetation index (NDVI) in the acquired imagery before and after the fires, we found that this method allowed simple identification of burned and unburned areas, which was not readily possible without applying the normalization method. Our result suggests that the developed approach can help fully exploit remote sensing datasets that have high spatial resolution and are acquired with high frequency but potentially contain radiometric inconsistencies in order to quickly identify land cover changes.

Automatic Detection of Trawl-Marks in Sidescan Sonar Images through Spatial Domain Filtering, Employing Haar-Like Features and Morphological Operations

Bottom trawl footprints are a prominent environmental impact of deep-sea fishery that was revealed through the evolution of underwater remote sensing technologies. Image processing techniques have been widely applied in acoustic remote sensing, but accurate trawl-mark (TM) detection is underdeveloped. The paper presents a new algorithm for the automatic detection and spatial quantification of TMs that is implemented on sidescan sonar (SSS) images of a fishing ground from the Gulf of Patras in the Eastern Mediterranean Sea. This method inspects any structure of the local seafloor in an environmentally adaptive procedure, in order to overcome the predicament of analyzing noisy and complex SSS images of the seafloor. The initial preprocessing stage deals with radiometric inconsistencies. Then, multiplex filters in the spatial domain are performed with multiscale rotated Haar-like features through integral images that locate the TM-like forms and additionally discriminate the textural characteristics of the seafloor. The final TMs are selected according to their geometric and background environment features, and the algorithm successfully produces a set of trawling-ground quantification values that could be established as a baseline measure for the status assessment of a fishing ground.

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data

Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of land surface monitoring and characterization for more than four decades. However, a limitation of conventional single-sensor satellite missions is their limited capacity to observe land surface dynamics at the very high spatial and temporal resolutions demanded by a wide range of applications. One solution to this spatio-temporal divide is an observation strategy based on the CubeSat standard, which facilitates constellations of small, inexpensive satellites. Repeatable near-daily image capture in RGB and near-infrared (NIR) bands at 3–4 m resolution has recently become available via a constellation of >130 CubeSats operated commercially by Planet. While the observing capacity afforded by this system is unprecedented, the relatively low radiometric quality and cross-sensor inconsistencies represent key challenges in the realization of their full potential as a game changer in Earth observation. To address this issue, we developed a Cubesat Enabled Spatio-Temporal Enhancement Method (CESTEM) that uses a multi-scale machine-learning technique to correct for radiometric inconsistencies between CubeSat acquisitions. The CESTEM produces Landsat 8 consistent atmospherically corrected surface reflectances in blue, green, red, and NIR bands, but at the spatial scale and temporal frequency of the CubeSat observations. An application of CESTEM over an agricultural dryland system in Saudi Arabia demonstrated CubeSat-based reproduction of Landsat 8 consistent VNIR data with an overall relative mean absolute deviation of 1.6% or better, even when the Landsat 8 and CubeSat acquisitions were temporally displaced by >32 days. The consistently high retrieval accuracies were achieved using a multi-scale target sampling scheme that draws Landsat 8 reference data from a series of scenes by using MODIS-consistent surface reflectance time series to quantify relative changes in Landsat-scale reflectances over given Landsat-CubeSat acquisition timespans. With the observing potential of Planet's CubeSats approaching daily nadir-pointing land surface imaging of the entire Earth, CESTEM offers the capacity to produce daily Landsat 8 consistent VNIR imagery with a factor of 10 increase in spatial resolution and with the radiometric quality of actual Landsat 8 observations. Realization of this unprecedented Earth observing capacity has far reaching implications for the monitoring and characterization of terrestrial systems at the precision scale.

Patch-Based Information Reconstruction of Cloud-Contaminated Multitemporal Images

Cloud covers, which are generally present in optical remote sensing images, limit the usage of acquired images and increase the difficulty in data analysis. Thus, information reconstruction of cloud-contaminated images generally plays an important role in image analysis. This paper proposes a novel method to reconstruct cloud-contaminated information in multitemporal remote sensing images. Based on the concept of utilizing temporal correlation of multitemporal images, we propose a patch-based information reconstruction algorithm that spatiotemporally segments a sequence of images into clusters containing several spatially connected components called patches and then clones information from cloud-free and high-similarity patches to their corresponding cloud-contaminated patches. In addition, a seam that passes through homogenous regions is used in information reconstruction to reduce radiometric inconsistency, and information cloning is solved using an optimization process with the determined seam. These processes enable the proposed method to well reconstruct missing information. Qualitative analyses of image sequences acquired by a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and a quantitative analysis of simulated data with various cloud contamination conditions are conducted to evaluate the proposed method. The experimental results demonstrate the superiority of the proposed method to related methods in terms of radiometric accuracy and consistency, particularly for large clouds in a heterogeneous landscape.

도심지역의 그림자 영향을 고려한 다시기 고해상도 위성영상의 선택적 히스토그램 매칭

도심지 모델링과 분석을 효과적으로 수행하기 위해서는 다른 시기나 다른 지역의 추가적인 고해상도 위성영상이 반드시 필요하다. 그러나 같은 지상 개체라 하더라도 서로 다른 영상에서 방사적인 불일치가 존재하며 이는 영상 처리와 분석의 정확도를 저하시키는 원인이 된다. 더욱이 도심지의 경우 건물, 수목, 교량, 기타 구조물 등 높이를 갖는 개체들은 영상 전체에 걸쳐 그림자를 발생시키며 이는 상대 방사 정규화의 질을 저하시킨다. 본 연구에서는 태양과 위성의 기하학적 위치 정보, 부가적인 수치 표고 모델이 없어도 적용이 가능한 단영상 기반의 그림자 추출기법을 적용하고 그림자의 영향을 배제한 선택적인 히스토그램 매칭 기법을 제안하였다. 건물의 에지 버퍼 영역에 대한 인접 정보와 분할을 통해 생성된 객체의 공간 및 분광인자를 이용하여 그림자를 추출한 후, 아스팔트 도로와 같이 그림자로 잘못 추출된 이상 객체를 제거하였다. 최종적으로 그림자 지역이 마스킹 된 Quickbird-2 다시기 영상을 이용하여 비그림자 지역만을 이용하여 선택적 히스토그램 매칭을 수행하였다. Additional high resolution satellite images, other period or site, are essential for efficient city modeling and analysis. However, the same ground objects have a radiometric inconsistency in different satellite images and it debase the quality of image processing and analysis. Moreover, in an urban area, buildings, trees, bridges, and other artificial objects cause shadow effects, which lower the performance of relative radiometric normalization. Therefore, in this study, we exclude shadow areas and suggest the selective histogram matching methods for image based application without supplementary digital elevation model or geometric informations of sun and sensor. We extract the shadow objects first using adjacency informations with the building edge buffer and spatial and spectral attributes derived from the image segmentation. And, Outlier objects like a asphalt roads are removed. Finally, selective histogram matching is performed from the shadow masked multi-temporal Quickbird-2 images.

Radiometric framework for image mosaicking

Nonuniform exposures often affect imaging systems, e.g., owing to vignetting. Moreover, the sensor's radiometric response may be nonlinear. These characteristics hinder photometric measurements. They are particularly annoying in image mosaicking, in which images are stitched to enhance the field of view. Mosaics suffer from seams stemming from radiometric inconsistencies between raw images. Prior methods feathered the seams but did not address their root cause. We handle these problems in a unified framework. We suggest a method for simultaneously estimating the radiometric response and the camera nonuniformity, based on a frame sequence acquired during camera motion. The estimated functions are then compensated for. This permits image mosaicking, in which no seams are apparent. There is no need to resort to dedicated seam-feathering methods. Fundamental ambiguities associated with this estimation problem are stated.