Relative Radiometric Normalization Method Research Articles

LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images.

Relative radiometric normalization (RRN) is a critical pre-processing step that enables accurate comparisons of multitemporal remote-sensing (RS) images through unsupervised change detection. Although existing RRN methods generally have promising results in most cases, their effectiveness depends on specific conditions, especially in scenarios with land cover/land use (LULC) in image pairs in different locations. These methods often overlook these complexities, potentially introducing biases to RRN results, mainly because of the use of spatially aligned pseudo-invariant features (PIFs) for modeling. To address this, we introduce a location-independent RRN (LIRRN) method in this study that can automatically identify non-spatially matched PIFs based on brightness characteristics. Additionally, as a fast and coregistration-free model, LIRRN complements keypoint-based RRN for more accurate results in applications where coregistration is crucial. The LIRRN process starts with segmenting reference and subject images into dark, gray, and bright zones using the multi-Otsu threshold technique. PIFs are then efficiently extracted from each zone using nearest-distance-based image content matching without any spatial constraints. These PIFs construct a linear model during subject-image calibration on a band-by-band basis. The performance evaluation involved tests on five registered/unregistered bitemporal satellite images, comparing results from three conventional methods: histogram matching (HM), blockwise KAZE, and keypoint-based RRN algorithms. Experimental results consistently demonstrated LIRRN's superior performance, particularly in handling unregistered datasets. LIRRN also exhibited faster execution times than blockwise KAZE and keypoint-based approaches while yielding results comparable to those of HM in estimating normalization coefficients. Combining LIRRN and keypoint-based RRN models resulted in even more accurate and reliable results, albeit with a slight lengthening of the computational time. To investigate and further develop LIRRN, its code, and some sample datasets are available at link in Data Availability Statement.

Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images

ABSTRACT In some remote sensing applications, such as unsupervised change detection, bitemporal multispectral images must be first aligned/harmonized radiometrically. For doing so, Many Relative Radiometric Normalization (RRN) algorithms exist; however, most suffer from misregistration problems and can only operate on geo/co-registered image pairs, while unregistered multispectral pairs are required. To tackle this situation, keypoint-based RRN methods were introduced, which can radiometrically calibrate unregistered/registered image pairs using keypoint matching algorithms. However, they ignore the spatial and spectral characteristics of spectral bands of input images, resulting in potential RRN errors. They also employ a linear mapping function for RRN modelling, which can not handle non-linear radiometric distortions. To address these limitations, this paper proposes a robust algorithm for RRN of bitemporal multispectral images, using a new extension of SURF detector for multispectral images, namely the Weighted Spectral Structure Tensor SURF (WSST-SURF), and a flexible Switching Regression (SR) model. Taking advantage of the tensor theory, WSST-SURF efficiently preserves both spatial and spectral information distributed over all bands of multispectral images for keypoint detection, resulting in extracting reliable inliers (or keypoints) for RRN. An adaptive SR model is introduced based on the normalized mutual information, accurately approximating a linear/non-linear relationship between inliers in multispectral images. Six unregistered multispectral image pairs captured by inter/intra remote sensing sensors were employed to validate the efficacy of the proposed method. The results indicate that adopting spectral tensor-based SURF methods in the RRN process exhibits better local and global performance than using the original SURF. Furthermore, the proposed method outperforms the existing conventional RRN methods in terms of accuracy and visual quality, indicating its competence for RRN of bitemporal multispectral images with high illumination, viewpoint, and scale differences.

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.

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.

Random Sampling-Based Relative Radiometric Normalization

Relative radiometric normalization (RRN) is widely used for radiometric calibration of bitemporal multispectral images prior to any temporal analysis such as change detection. However, standard RRN methods are not robust against anomalous (or changed) pixels, which warp the calibration and decrease the spectral similarity of processed images. This letter proposes a novel random sample consensus-based RRN method, which only uses small pixel subsets to implement the linear mapping relationship for RRN, and does not require the calibration of its parameters. The experimental results show that the proposed method performs favorably against the widely used RRN methods in all metrics considered in this letter.

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.

HYBRID CANONICAL CORRELATION ANALYSIS AND REGRESSION FOR RADIOMETRIC NORMALIZATION OF CROSS-SENSOR SATELLITE IMAGES

Abstract. Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition condition. In this study, a cross-sensor RRN method is proposed for optical satellite images from Landsat 8 OLI (L8) and Landsat 7 ETM+ (L7) sensors. The data from these two sensors have different pixel depths. Therefore, a rescaling on the radiometry resolution is performed in the preprocessing. Then, multivariate alteration detection (MAD) based on kernel canonical correlation analysis (KCCA) is adopted, which is called KCCA-based MAD, to select pseudo-invariant features (PIFs). The process of RRN is performed by using polynomial regression with Gaussian weighted regression. In experiments, qualitative and quantitative analyses on images from different sensors are conducted. The experimental result demonstrates the superiority of the proposed nonlinear transformation, in terms of regression quality and radiometric consistency, compared with RRN using linear regression.

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.

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.

A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery

Relative radiometric normalization (RRN) of multi-temporal satellite images minimizes the radiometric discrepancies between two images caused by inequalities in the acquisition conditions rather than changes in surface reflectance. In this paper, a new automatic RRN method was developed based on regression theory comprising the following techniques: Automatic detection of unchanged pixels, Histogram modeling of subject images, and Calculation of linear transformation coefficients for various categories of pixels according to their gray values in each band. The proposed method applies a new idea for unchanged pixels selection which increases the accuracy and automation level of the detection process. Also, a new idea is proposed for categorizing pixels according to their gray values. In this method, the number and interval of the categories are determined automatically and independently based on the histogram of subject images for each band. Thus, divergent influences of effective parameters such as atmosphere on different gray values are modeled. The method was implemented on two images taken by the TM sensor. Normalization results acquired by the proposed method were compared with the six conventional methods including: histogram matching, haze correction, minimum-maximum, mean-standard deviation, simple regression, no-change and modified regression using unchanged pixels. Experimental results confirmed the effectiveness of the proposed method in the automatic detection of unchanged pixels and minimizing any imaging condition effects (i.e., atmosphere and other effective parameters).

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.

A new model for automatic normalization of multitemporal satellite images using Artificial Neural Network and mathematical methods

Relative Radiometric Normalization is often required in remote sensing image analyses particularly in the land cover change detection process. Normalization process minimizes the radiometric differences between two images caused by inequalities in the acquisition conditions rather than changes in surface reflectance. A wide range of RRN methods have been developed to adjust linear models. This paper proposes an automated Relative Radiometric Normalization (RRN) method to adjust a non-linear model based on an Artificial Neural Network (ANN) and unchanged pixels. The proposed method includes the following stages: (1) automatic detection of unchanged pixels based on a new idea that uses CVA (Change Vector Análysis) method, PCA (Principal Component Analysis) transformation and K-means clustering technique, (2) evaluation of different architectures of perceptron neural networks to find the best architecture for this specific task and (3) use of the aforementioned network for normalizing the subject image. The method has been implemented on two images taken by the TM sensor. Experimental results confirm the effectiveness of the presented technique in the automatic detection of unchanged pixels and minimizing imaging condition effects (i.e., atmosphere and other effective parameters).

Comparison of relative radiometric normalization methods using pseudo-invariant features for change detection studies in rural and urban landscapes

Relative radiometric normalization (RRN) to remove sensor effects, solar and atmospheric variation from at-sensor radiance values is often necessary for effective detection of temporal change. Traditionally, pseudo-invariant features (PIFs) are chosen subjectively, where as an analyst manually chooses known objects, often man-made, that should not change over time. An alternative method of selecting PIFs uses a principal component analysis (PCA) to select the PIFs. We compare the two RRN methods using PIFs in multiple Landsat images of urban and rural areas in Australia. An assessment of RRN quality was conducted including measurements of slope, root mean square error, and normalized difference vegetation index. We found that in urban areas both methods performed similarly well. However, in the rural area the automated PIF selection method using a PCA performed better due to the rarity of built features that are required for the manual PIF selection. We also found that differences in performance of the manual and automated methods were dependent on the accuracy assessment method tested. We conclude with a discussion on the relative merits of different RRN methods and practical advice on how to apply the automated PIF selection method.

Automatic relative radiometric normalization method based on SIFT feature matching

Automatic relative radiometric normalization method based on SIFT feature matching

Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery

Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. This paper demonstrates applicability of an empirical relative radiometric normalization method to a set of multitemporal cloudy images acquired by Resourcesat1 LISS III sensor. Objective of this study is to detect and remove cloud cover and normalize an image radiometrically. Cloud detection is achieved by using Average Brightness Threshold (ABT) algorithm. The detected cloud is removed and replaced with data from another images of the same area. After cloud removal, the proposed normalization method is applied to reduce the radiometric influence caused by non surface factors. This process identifies landscape elements whose reflectance values are nearly constant over time, i.e. the subset of non-changing pixels are identified using frequency based correlation technique. The quality of radiometric normalization is statistically assessed by R 2 value and mean square error (MSE) between each pair of analogous band. Keywords—Correlation, Frequency domain, Multitemporal, Relative Radiometric Correction I. INTRODUCTION ATELLITE images are useful for monitoring changes in land use and land cover. But major problem with these images is that regions below clouds are not covered by sensor. The image distortion due to cloud cover is a classical problem of visible band of remote sensing imagery. Especially, for non-stationary satellite, it is commonly found in the earth resource observation application. Removing cloud cover from satellite imagery is very useful for assisting image interpretation. Hence cloud detection and removal is very vital in processing of satellite imagery. Further it is more difficult to quantify and interpret changes on multitemporal images under different illumination, atmospheric or sensor conditions without radiometric calibration. The relative approach to radiometric correction, known as relative radiometric normalization, is preferred because no in situ atmospheric data at the time of satellite overpasses are required (1).

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI “cancellation effect” where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes.