On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery

Tang, Haoyu; Xie, Junfeng; Tang, Xinming; Chen, Wei; Li, Qi

doi:10.3390/rs14040886

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…Remote sensing can overcome the shortcomings of traditional forest resource surveys, and it has been widely used in the classification research of forest tree species [4]. Based on GF-6 images, Huang et al [5] used the random forest (RF) machine learning method to classify forest tree species, such as eucalyptus, pine trees, cedar, and other arbor forests, by calculating the vegetation index and optimizing the feature combination. The results showed that the model of optimized feature combination was the best, with an accuracy of 85.38%.…”

Section: Introductionmentioning

confidence: 99%

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

You

Huang

Qin

et al. 2022

Forests

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Most research on forest tree species classification based on optical image data uses information such as spectral reflectance, vegetation index, texture, and phenology data. However, owing to the limited spectral resolution of multispectral images and the high cost of hyperspectral data, there is room for improvement in the classification of tree species in large areas based on optical images. The combined application of multispectral images and other auxiliary data can provide a new method for improving tree species classification accuracy. Hence, Sentinel-2 images were used to extract spectral reflectance, spectral index, texture, and phenological information. Data for topography, precipitation, air temperature, ultraviolet aerosol index, NO2 concentration, and other variables were included as auxiliary data. Models for forest tree species classification were constructed through feature combination and feature optimization using the random forest (RF), gradient tree boost (GTB), support vector machine (SVM), and classification and regression tree (CART) algorithms. The classification results of 16 feature combinations with the 4 classification methods were compared, and the contributions of different features to the classification models of forest tree species were evaluated. Finally, the optimal classification model was selected to identify the spatial distribution of forest tree species in the study area. The model based on feature optimization gave the best results among the 16 feature combination models. The overall accuracy and kappa coefficient were increased by 18% and 0.21, respectively, compared with the spectral classification model, and by 17% and 0.20, respectively, compared with the spectral and spectral index classification model. By analyzing the feature optimization model, it was found that terrain, ultraviolet aerosol index, and phenological information ranked as the top three features in terms of importance. Although the importance of spectral reflectance and spectral index features was lower, the number of feature variables accounted for a large proportion of the total. The importance of commonly used texture features was limited, and these features were not present in the feature optimization model. The RF algorithm had the highest classification accuracy, with an overall accuracy of 82.69% and a kappa coefficient of 0.80, among the four classification algorithms. The results of GTB were close to those of RF, and the difference in overall classification accuracy was only 0.14%. However, the results of the SVM and CART algorithms were relatively weaker, with overall classification accuracies of about 70%. It can be concluded that the combined application of Sentinel-2 images and auxiliary data can improve forest tree species classification accuracy. The model based on feature optimization achieved the highest classification accuracy among the 16 feature combination models. The spectral reflectance and spectral index data extracted from optical images are useful for tree species classification, but the effect of texture features was very limited. Auxiliary data, such as topographic features, ultraviolet aerosol index, phenological features, NO2 concentration features, topographic diversity features, precipitation features, temperature features, and multi-scale topographic location index data, can effectively improve forest tree species classification accuracy. The RF algorithm had the highest accuracy, and it can be used for tree species classification space distribution identification. The combined application of Sentinel-2 images and auxiliary data can improve classification accuracy, but the highest accuracy of the model was only 82.69%, which leaves room for improvement. Thus, more effective auxiliary data and the vertical structural parameters extracted from satellite LiDAR can be combined with multispectral images to improve forest tree species classification accuracy in future research.

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Section: Introductionmentioning

confidence: 99%

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

You

Huang

Qin

et al. 2022

Forests

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“…The second one adopts a large-sized testing chamber for double column tank, and the inner wall material is made of black acrylic plate with high absorption and low reflection, which effectively suppresses the inner reflection. The principle of eliminating stray light in the double column tank is "absorption+ reflection", shown in Figure1 [23], [24]. Most of the light entering the double column tank can be absorbed by the acrylic plate, and the unabsorbed light leaves the test light path in the form of mirror reflection, thus playing a dual role of "removing stray light".…”

Section: Working Principle Of Stray Light Test Systemmentioning

confidence: 99%

Study on Stray Light Testing and Suppression Techniques for Large-Field of View Multispectral Space Optical Systems

Lu,

Xu,

Zhang

et al. 2024

IEEE Access

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To evaluate the ability of space optical systems to suppress off-axis stray light, this paper proposes a stray light testing method for large-field of view, multispectral spatial optical systems based on point source transmittance (PST). And a stray light testing platform was developed using a high-brightness simulated light source, large-aperture off-axis reflective collimator, high-precision positioning mechanism and a double column tank to evaluate the stray light PST index of spatial optical system. On the basis of theoretical analyses, a set of calibration lenses and stray light elimination structures such as hoods, baffle and stop are designed for the accuracy calibration of stray light testing systems. The theoretical PST values of the calibration lens at different off-axis angles are analyzed by Trace Pro software simulation and compared with the measured values to calibrate the accuracy of the system. The testing results show that the PST measurement range of the system reaches 10 -3 ~10 -10 when the off-axis angles of the calibration lens are in the range of ±5°±60°. The stray light test system has the advantages of wide working band, high automation and large dynamic range, and its test results can be used in the correction of lens hood and other applications.INDEX TERMS Multi-spectrum, large-field view, stray light, point source transmittance (PST), lens hood

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“…An area with 8×8 pixels for each target region was selected for analysis. The white, gray, and black targets were analyzed, and their measured reflectance values in literature 31 were used for the computations. Then, the simulations were performed for the provided inputs with the images.…”

Section: Experimental Validationsmentioning

confidence: 99%

“…E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 3 1 ; 1 1 6 ; 8 7 Signal ¼ Image mean − Dark image mean:(31) …”

mentioning

confidence: 99%

Signal-to-noise ratio model in Python for high-resolution space-borne electro-optic imagers

Öztürk

Başlar

Selimoglu

et al. 2023

J. Appl. Rem. Sens.

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.We present a signal-to-noise ratio (SNR) simulator design for high-resolution Earth observation imagers with time-delay and integration (TDI) imaging sensors. SNR is one of the space-borne electro-optic imagers’ driving design and performance evaluation parameters. TDI line imaging sensors are used to meet these imagers’ SNR requirements by increasing effective exposure time during imaging. Determining the optimum number of TDI stages to meet the SNR requirements of the mission is critical for the imaging sensor selection, optical design of the telescope, laboratory radiometric tests, and imaging in orbit. The SNR simulator is based on an end-to-end image chain calculation following the light pathway from the sun to the imager. The simulator comprises radiance, telescope, sensor, and image modules written in the open-access Python environment. The radiance module uses PcModWin/MODerate resolution atmospheric TRANsmission output data of the atmospheric radiance calculation input for the simulator. The simulator computes the SNR for the given telescope and imaging sensor model and simulates associated images to show the radiometric performance. A high-resolution electro-optic imager model is used to demonstrate the performance and versatility of the simulator for the case studies. Experimental simulator validation is demonstrated for the images acquired by the Göktürk-2 space-borne imager.

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On-Orbit Radiometric Performance of GF-7 Satellite Multispectral Imagery

Cited by 7 publications

References 25 publications

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

Study on Stray Light Testing and Suppression Techniques for Large-Field of View Multispectral Space Optical Systems

Signal-to-noise ratio model in Python for high-resolution space-borne electro-optic imagers

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