Radiometric Normalization Method Research Articles

An Evaluation of Radiometric Normalization Methods for Gaofen-2 Vegetation Index Mapping

An Evaluation of Radiometric Normalization Methods for Gaofen-2 Vegetation Index Mapping

Abandoned farmland detection using single-year satellite images in Japan

Mapping abandoned farmlands' location and spatial pattern is essential for rural planning. Monitoring small abandoned farmland based on dynamic farmland's phenology is challenging to due to conflict between spatial and temporal resolutions of conventional satellite missions. A unique approach that combines satellite constellation imagery with improved automatic radiometric normalization method for more temporally consistent reflectance were proposed. Applying it in Ami-town, Ibaraki Prefecture, Japan, abandoned farmlands were identified with 3-m resolution and a Kappa coefficient of 0.81 based on satellite constellation-based time-series data; however, the discrimination of the degree of abandonment was challenging. Unlike conventional abandoned farmland mapping approaches which targets spatially large area, our approach provides information at small, fragmented and abandoned farmland in East Asia that enables labor-saving monitoring of land use shifts.

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.

Radiometric Normalization for Cross-Sensor Optical Gaofen Images with Change Detection and Chi-Square Test

As the number of cross-sensor images increases continuously, the surface reflectance of these images is inconsistent at the same ground objects due to different revisit periods and swaths. The surface reflectance consistency between cross-sensor images determines the accuracy of change detection, classification, and land surface parameter inversion, which is the most widespread application. We proposed a relative radiometric normalization (RRN) method to improve the surface reflectance consistency based on the change detection and chi-square test. The main contribution was that a novel chi-square test automatically extracts the stably unchanged samples between the reference and subject images from the unchanged regions detected by the change-detection method. We used the cross-senor optical images of Gaofen-1 and Gaofen-2 to test this method and four metrics to quantitatively evaluate the RRN performance, including the Root Mean Square Error (RMSE), spectral angle cosine, structural similarity, and CIEDE2000 color difference. Four metrics demonstrate the effectiveness of our proposed RRN method, especially the reduced percentage of RMSE after normalization was more than 80%. Comparing the radiometric differences of five ground features, the surface reflectance curve of two Gaofen images showed more minor differences after normalization, and the RMSE was smaller than 50 with the reduced percentages of about 50–80%. Moreover, the unchanged feature regions are detected by the change-detection method from the bitemporal Sentinel-2 images, which can be used for RRN without detecting changes in subject images. In addition, extracting samples with the chi-square test can effectively improve the surface reflectance consistency.

Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features

In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs.

A Nonlinear Radiometric Normalization Model for Satellite Imgaes Time Series Based on Artificial Neural Networks and Greedy Algroithm

Satellite Image Time Series (SITS) is a data set that includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two or three scenes in different time phases). However, these methods are unsuitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions, which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, we propose a nonlinear radiometric normalization model (NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algorithm (GA). In this method, GA is used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoids the selection of a single reference image. ANN is used to obtain the optimal solution of error function, which minimizes the radiometric distortion between different images in SITS. The SITS composed of 21 Landsat-8 images in Tianjin, China, from October 2017 to January 2019 was selected to test the method. We compared NMAG with other two contrast methods (Contrast Method 1 (CM1) and Contrast Method 2 (CM2)), and found that the average root mean square error (μRMSE) of NMAG (497.22) is significantly smaller than those of CM1 (641.39) and CM2 (543.47), and the accuracy of normalized SITS obtained using NMAG increases by 22.4% and 8.5% compared with CM1 and CM2, respectively. These experimental results confirm the effectiveness of NMAG in reducing radiometric distortion caused by continuously changing noise between images in SITS.

Radiometric Normalization of Multitemporal Landsat and Sentinel-2 Images Using a Reference MODIS Product Through Spatiotemporal Filtering

Radiometric normalization is an essential preprocessing step for almost all remote sensing applications such as change detection, image mosaic, and 3-D reconstruction. This article proposes a novel radiometric normalizing method based on spatiotemporal filtering using a reference moderate resolution imaging spectroradiometer (MODIS) product. This differs from traditional relative radiometric normalization (RRN) methods in two folds: first, the number of reference images is more than one, which introduces more complexities than RRN with a single reference image; second, the resolution of MODIS product is significantly lower, thus requiring the algorithms to accommodate scale differences. To address, our approach extends the traditional spatiotemporal filtering method with per image bias that represents both internal (e.g., sensor characteristics) and external (e.g., atmosphere and topography) against the reference data. In addition, we use the Kullback-Leibler divergence metric to statistically determine the resemblance degree between the temporal images for weighting. We applied our proposed method to normalize Landsat Operational Land Imager, Enhanced Thematic Mapper Plus +, and Sentinel MSI using MODIS Nadir BRDF-adjusted reflectance product, covering two study areas of 30 × 15 km2 and 32 × 52 km2, respectively, and we show a notable radiometric consistency over both temporal and spatial dimension after the processing through three comparative experiments with state-of-the-art methods. 1) 3–7% improvement in the contexts of transfer learning, which favors only images with consistent radiometric properties and 2) Mosaic results using our processed images show no apparent seamlines as compared with images processed by other methods.

Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images

This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.

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.

A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.

Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

Determining appropriate master images, reducing radiometric error accumulation, and eliminating outliers from the cloud, water, and land changes, are three main issues in radiometric normalization of multitemporal high-resolution satellite images (HRSI) during mosaicking. However, these three issues have not been simultaneously considered by the existing methods. This article presents a comprehensive radiometric normalization method for multitemporal HRSI using a radiometric block adjustment without master images. Pseudoinvariant features (PIFs) extracted from image pairs using the iteratively reweighted multivariate alteration detection are used as the corresponding pixel observations and organized to form radiometric tie points according to the corresponding horizontal space coordinates. Radiometric error equations are subsequently constructed, and the linear radiometric transformation parameters are solved by a global adjustment. The time-invariant PIFs generally represent the true corresponding features and naturally avoid the cloud, water, and land changes, which can eliminate the effects of outliers. Furthermore, the pixel values of tie points calculated from the weighted average of the corresponding pixel observations are used as virtual radiometric control points to eliminate the dependency on master images. Moreover, a global optimum can be achieved by the global adjustment, effectively overcoming the error accumulation, which is severe in large datasets. Four groups of HRSI datasets from various satellites are used to validate the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms two state-of-the-art methods and has good applicability and stability, considering both visual effects and quantitative performance.

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

Radiometric normalization is an essential preprocessing step that must be performed to detect changes in multi-temporal satellite images and, in general, relative radiometric normalization is utilized. However, most relative radiometric normalization methods assume a linear relationship and they cannot take into account nonlinear properties, such as the distribution of the earth’s surface or phenological differences that are caused by the growth of vegetation. Thus, this paper proposes a novel method that assumes a nonlinear relationship and it uses a representative nonlinear regression model—multilayer perceptron (MLP). The proposed method performs radiometric resolution compression while considering both the complexity and time cost, and radiometric control set samples are extracted based on a no-change set method. Subsequently, the spectral index is selected for each band to compensate for the phenological properties, phenological normalization is performed based on MLP, and the global radiometric properties are adjusted through postprocessing. Finally, a performance evaluation is conducted by comparing the results herein with those from conventional relative radiometric normalization algorithms. The experimental results show that the proposed method outperforms conventional methods in terms of both visual inspection and quantitative evaluation. In other words, the applicability of the proposed method to the normalization of multi-temporal images with nonlinear properties is confirmed.

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.

Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery

Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using multitemporal satellite images due to uncertainties of at-sensor radiances caused by different sun angles and atmospheric conditions. In case the atmospheric model and ground measurements are unavailable during data acquisitions, relative normalization is an alternative method which minimizes the radiometric differences among images without the requirement of additional information. The keys to a successful relative normalization are the selection of pseudo invariant features (PIFs) from bitemporal images and the regression of selected PIFs for transformation coefficient determination. Previous studies on transformation coefficient determination adopted band-by-band regression. These studies have obtained satisfactory normalization results; however, they have not fully considered the spectral inconsistency problem caused by individual band regression. To alleviate this problem, this study proposed a constrained orthogonal regression, which enforces pixel spectral signatures to be as consistent as possible during radiometric normalization while band regression quality is preserved. In addition, instead of selecting one of the input images as reference for radiometric transformation, a common radiometric level located between bitemporal images is selected as the reference to further reduce possible spectral inconsistency. Qualitative and quantitative analyses of several bitemporal images acquired by the Landsat 8 sensor were conducted to evaluate the proposed method with the measurements of spectral distance and similarity. The experimental results demonstrate the superiority of the proposed method to related regression and radiometric normalization methods, in terms of spectral signature consistency.

KCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for the first time to overcome this problem, which leads to a new kernel method. It can maximally reduce the image differences among multi-temporal images regardless of the imaging conditions and the reflectivity difference. It also perfectly eliminates the impact of nonlinear changes caused by seasonal variation of natural objects. Comparisons with the multivariate alteration detection (CCA-based) normalization and the histogram matching, on Gaofen-1 (GF-1) data, indicate that the kCCA-based normalization can preserve more similarity and better correlation between an image-pair and effectively avoid the color error propagation. The proposed method not only builds the common scale or reference to make the radiometric consistency among GF-1 image sequences, but also highlights the interesting spectral changes while eliminates less interesting spectral changes. Our method enables the application of GF-1 data for change detection, land-use, land-cover change detection etc.

A Mixed Radiometric Normalization Method for Mosaicking of High-Resolution Satellite Imagery

A new mixed radiometric normalization (MRN) method is introduced in this paper which aims to eliminate the radiometric difference in image mosaicking. The radiometric normalization methods can be classified as the absolute and relative approaches in traditional solutions. Though the absolute methods could get the precise surface reflectance values of the images, rigorous conditions required for them are usually difficult to obtain, which makes the absolute methods impractical in many cases. The relative methods, which are simple and practicable, are more widely applied. However, the standard for designating the reference image needed for these methods is not unified. Moreover, the color error propagation and the two-body problems are common obstacles for the relative methods. The proposed MRN approach combines absolute and relative radiometric normalization methods, by which the advantages of both can be fully used and the limitations can be effectively avoided. First, suitable image after absolute radiometric calibration is selected as the reference image. Then, the invariant feature probability between the pixels of the target image and that of the reference image is obtained. Afterward, an adaptive local approach is adopted to obtain a suitable linear regression model for each block. Finally, a bilinear interpolation method is employed to obtain the radiometric calibration parameters for each pixel. Moreover, the CIELAB color space is adopted to evaluate the results quantitatively. Experimental results of ZY-3, GF-1, and GF-2 data indicate that the proposed method can eliminate the radiometric differences between images from the same or even different sensors.

Relative Radiometric Normalization for Multitemporal Remote Sensing Images by Hierarchical Regression

The existing relative radiometric normalization methods are insufficient to define the invariant pixels automatically, and the conventional methods do not perform well when the multitemporal images contain a lot of changes. Two types of changes should be particularly considered: one is caused by significant spectral differences due to change of ground objects, and the other is the pixels in the regions of misalignment caused by displacement due to differences in acquisition view angles and geometrical distortions. To automatically extract invariant pixels and reduce the influence of the changes, a hierarchical regression method is proposed to reduce the radiation difference for multitemporal images, which consists of extraction of the pseudo-invariant features (PIFs) and optimization of normalization parameters. A weighted regression based on spectral difference is proposed to automatically extract the PIFs, which can also suppress the negative effect of the first type of changes. In addition, a robust regression with gradient dependence is performed on the extracted PIFs to build the final relationship between the target image and the reference image, which can be robust for the second type of changes. Experimental results demonstrate that the proposed method has a better performance to normalize the target image.

Automatic Radiometric Normalization for Multitemporal Remote Sensing Imagery With Iterative Slow Feature Analysis

Multitemporal imagery analysis has attracted widespread interest in recent years due to the large number of applications. Multitemporal remote sensing imagery analysis is very important for Earth observation, in order to allow an understanding of the relationships and interactions between human and natural phenomena. Radiometric variance of the same targets due to differences in environmental conditions is one of the most important issues. In this paper, we propose an automatic radiometric normalization method with iterative slow feature analysis (ISFA) to reduce the radiometric variance. Slow feature analysis extracts invariant features from the quickly varying input signals. It is first reformulated for the multitemporal imagery problem and then improved to an iterative version. In the iteration, high weights are assigned to unchanged pixels. After convergence, the linear function of the radiometric normalization is directly obtained with all the pixels and their weights. If the ISFA is negatively affected by the changed pixels in some special cases and cannot find the correct regression line, initial seeds are selected as the initial weights in the iteration, to improve the performance, which is called S-ISFA. Two pairs of multitemporal ETM images from different seasons and years were used to test the effectiveness of our proposed method. The quantitative evaluation showed that our proposed method performs better, with smaller differences in the statistical distributions and radiometric values than the other state-of-the-art methods. The robustness with regard to the selection of initial seeds was also proved in the experiment.

Algorithm for relative radiometric consistency process of remote sensing images based on object-oriented smoothing and contourlet transforms

Traditional relative radiometric normalization methods generally depend on global statistical linear parameters, do not consider two-dimensional radiometric distribution, and do not eliminate foreground objects in an image. Thus, we present a method for relative radiometric consistency process based on object-oriented smoothing and contourlet transforms. Object-level smoothing is applied to both the reference image and the image to be normalized so as to reduce the influence of foreground objects on background radiation extraction. Then, high-frequency and low-frequency sections of an image are separated by contourlet transforms to preserve high-frequency texture information of the image to be normalized, with low-pass filtering applied to the low-frequency sections to gather the background radiation difference. Finally, contourlet reverse transforms are used to reconstruct the radiometrically normalized images. Test results show that the proposed method is effective for radiometric normalization of images with both large-scale and small-scale radiometric characteristics. The proposed method can not only normalize linear and nonlinear radiation differences at the same time, but also maximally preserve image texture information. It can improve the visual effects of normalized images and increase change detection accuracy.