A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features

Zhou, Huizhen; Liu, Shaomin; Jiang, He; Wen, Quan; Song, Lisheng; Ma, Yanfei

doi:10.1080/01431161.2016.1213922

Cited by 32 publications

(35 citation statements)

References 30 publications

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“…As for OLI data, the atmospheric correction method we used was the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) algorithm [48]. The atmospheric correction method of the HJ1A-CCD1 data is the relative radiation correction based on Pseudo Invariant Features (PIF) [49], a reference to the radiation-corrected OLI data. The geometric correction method of the HJ1A-CCD1 data is the rational function model (RFM) [50], with the OLI image for geo-reference [51].…”

Section: Results and Analysismentioning

confidence: 99%

Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

Yan

Wang

2016

Remote Sensing

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Abstract:With the arrival of the big data era in Earth observation, the remote sensing communities have accumulated a large amount of invaluable and irreplaceable data for global monitoring. These massive remote sensing data have enabled large-area and long-term series Earth observation, and have, in particular, made standard, automated product generation more popular. However, there is more than one type of data selection for producing a certain remote sensing product; no single remote sensor can cover such a large area at one time. Therefore, we should automatically select the best data source from redundant multisource remote sensing data, or select substitute data if data is lacking, during the generation of remote sensing products. However, the current data selection strategy mainly adopts the empirical model, and has a lack of theoretical support and quantitative analysis. Hence, comprehensively considering the spectral characteristics of ground objects and spectra differences of each remote sensor, by means of spectrum simulation and correlation analysis, we propose a suitability evaluation model for product generation. The model will enable us to obtain the Production Suitability Index (PSI) of each remote sensing data. In order to validate the proposed model, two typical value-added information products, NDVI and NDWI, and two similar or complementary remote sensors, Landsat-OLI and HJ1A-CCD1, were chosen, and the verification experiments were performed. Through qualitative and quantitative analysis, the experimental results were consistent with our model calculation results, and strongly proved the validity of the suitability evaluation model. The proposed production suitability evaluation model could assist with standard, automated, serialized product generation. It will play an important role in one-station, value-added information services during the big data era of Earth observation.

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Section: Results and Analysismentioning

confidence: 99%

Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

Yan

Wang

2016

Remote Sensing

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“…The spectral indices are considered as features in this study, which is influenced by phenological properties [39][40][41]. At this time, the spectral indices calculated through reflectance, such as the normalized difference vegetation index, soil-adjusted vegetation index, or enhanced vegetation index, cannot be utilized, since the relative radiometric normalization is usually performed using the DN [3,41,42]. Therefore, the proposed method utilizes spectral indices that can be calculated through the DN values, among which the greenness indices of the excess green index (ExG), excess green minus excess red index (ExGR), vegetative index (VEG), color index of vegetation extraction (CIVE), and combined index (COM) are selected [43][44][45].…”

Section: Selection Of the Spectral Indexmentioning

confidence: 99%

“…With the development of various high-resolution satellite sensors, it is possible to precisely monitor the surface of the earth [1]. In particular, multi-temporal satellite images are an effective data source that can detect changes in land use and land cover [2,3]; however, it is difficult to maintain radiometric consistency in multi-temporal images due to changes in atmospheric conditions, sensor-target-illumination geometry, sensor calibration, and differences in phenological conditions [4,5]. Therefore, radiometric normalization, which is a preprocessing procedure, is performed to reduce-or eliminate-radiometric differences due to the aforementioned influences and increase sensitivity to landscape changes [6,7].…”

Section: Introductionmentioning

confidence: 99%

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

Seo

2019

Applied Sciences

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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. Relative radiometric normalization, on the other hand, is an image-based normalization method that selects one image as a reference and matches the radiometric characteristics of a subject image [13,14]. That is, relative radiometric normalization is generally utilized since the multi-temporal images are normalized into common scales without extra parameters [12,15].Relative radiometric normalization can be classified into two categories: a global statistics-based method and a radiometric control set sample (RCSS)-based method. The former is performed by using statistical values of all pixels in the image, which includes histogram matching, minimum-maximum (MM), mean-standard deviation (MS), and simple regression (SR) [15,16]. In contrast, the latter is based on invariant pixels, known as RCSSs, which include dark set-bright set (DB), pseudo invariant features (PIF), and no-change sets (NC) [17][18][19][20]. At this time, except for histogram matching, normalization is performed through regression equations, while assuming a linear relationship between the pixels at the same position in each band [21]. However, most remote sensing data are nonlinearly distributed, and the surface of the earth is composed of a complex mixture of natural and man-made features that exhibit nonlinear characteristics [22][23][24]. Changes that are caused by vegetation in particular have the most typical characteristics, including nonlinearity, which induces serious disturbances when change detection is performed [25,26]. In addition, since optical sa...

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“…The real hazy images were initially examined to check whether having uniform haze or not. If the haze was uniform, the weighted haze mean was to be estimated based on pseudoinvariant features (PIF) technique and subtracted from the hazy images [15]. On the other hand, if the haze was not uniform, the haze was to be segmented first using minimum noise fraction (MNF) technique.…”

Section: A Haze Removalmentioning

confidence: 99%

Haze Effects on Satellite Remote Sensing Imagery and their Corrections

Ahmad¹,

Quegan²,

Sufahani³

et al. 2019

IJACSA

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Imagery recorded using satellite sensors operating at visible wavelengths can be contaminated by atmospheric haze that originates from large scale biomass burning. Such issue can reduce the reliability of the imagery and therefore having an effective method for removing such contamination is crucial. The principal aim of this study is to investigate the effects of haze on remote sensing imagery and develop a method for removing them. In order to get a better understanding on the behaviour of haze, the effects of haze on satellite imagery were initially studied. A methodology of removing haze based on haze subtraction and filtering was then developed. The developed haze removal method was then evaluated by means of signal-to-noise ratio (SNR) and classification accuracy. The results show that the haze removal method is able to improve the haze-affected imagery qualitatively and quantitatively.

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A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features

Cited by 32 publications

References 30 publications

Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

Suitability Evaluation for Products Generation from Multisource Remote Sensing Data

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

Haze Effects on Satellite Remote Sensing Imagery and their Corrections

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