Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

Guo, Yiqing; Jia, Xiuping; Paull, David

doi:10.1109/tgrs.2017.2689798

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…Furthermore, additional research is needed to evaluate for sparse canopies and other land-cover types for different ecosystems. Although a combination of the RTM and LTR fixed kernels was chosen in our study, recent studies demonstrate that an adaptive kernels selection approach can improve the accuracy of BRDF models for different land-cover/structural types compared to fixed kernels [23,31]. In addition, the fitting method for BRDF modeling might also affect the correction result as mentioned in [22], especially for non-forest cover types such as wetlands and snow covered surfaces.…”

Section: Future Developmentsmentioning

confidence: 99%

“…It is worth mentioning that an angular normalization procedure is mandatory to reduce the confounding non-biophysical signals and to sustain the interpretation of the airborne imagery in respect to land surface properties. VHR airborne hyperspectral images acquired over a rugged topography requires disentanglement of soil, vegetation canopy, and relief BRDFs, which remains challenging [21][22][23]. The problem is even more complex with airborne data due to the strong influence of the local slope and aspect.…”

Section: Introductionmentioning

confidence: 99%

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A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

et al. 2020

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Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.

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Section: Future Developmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

et al. 2020

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“…The affected vines in this area are situated in a small valley (Figure 14), where terrain roughness and the presence of surrounding shadows significantly contribute to the enhancement of the BRDF effect, which, in turn, results in a noticeable influence on the recorded reflectance. The intrinsic relationship between topography and BRDF has been previously documented in the literature [42][43][44][45][46][47]. In this context, the western slope stands as an illustrative case of how the interaction between terrain morphology and environmental conditions can modulate the behaviour of the BRDF and, consequently, the reflectance in the study area.…”

Section: Discussionmentioning

confidence: 67%

Radiometric Improvement of Spectral Indices Using Multispectral Lightweight Sensors Onboard UAVs

Andrés-Anaya,

Molada-Tebar,

Hernández-López

et al. 2024

Drones

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Close-range remote sensing techniques employing multispectral sensors on unoccupied aerial vehicles (UAVs) offer both advantages and drawbacks in comparison to traditional remote sensing using satellite-mounted sensors. Close-range remote sensing techniques have been increasingly used in the field of precision agriculture. Planning the flight, including optimal flight altitudes, can enhance both geometric and temporal resolution, facilitating on-demand flights and the selection of the most suitable time of day for various applications. However, the main drawbacks stem from the lower quality of the sensors being used compared to satellites. Close-range sensors can capture spectral responses of plants from multiple viewpoints, mitigating satellite remote sensing challenges, such as atmospheric interference, while intensifying issues such as bidirectional reflectance distribution function (BRDF) effects due to diverse observation angles and morphological variances associated with flight altitude. This paper introduces a methodology for achieving high-quality vegetation indices under varied observation conditions, enhancing reflectance by selectively utilizing well-geometry vegetation pixels, while considering factors such as hotspot, occultation, and BRDF effects. A non-parametric ANOVA analysis demonstrates significant statistical differences between the proposed methodology and the commercial photogrammetric software AgiSoft Metashape, in a case study of a vineyard in Fuente-Alamo (Albacete, Spain). The BRDF model is expected to substantially improve vegetation index calculations in comparison to the methodologies used in satellite remote sensing and those used in close-range remote sensing.

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“…This phenomenon is often referred to as cross-image dataset shifts [10]. The shifts can either originate from changes in the nature of land surface properties, for example, induced by the phenology of vegetation, or from the background noise caused by varied acquisition and atmospheric conditions [11]- [13] and inconsistent sun-targetsenor geometries [14], [15]. Therefore, appropriate domainadaptation strategies need to be applied to tackle the dataset shifts [16]- [21].…”

Section: Introductionmentioning

confidence: 99%

Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification

Guo,

Jia,

Paull

2017

Preprint

Self Cite

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The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

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Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

Cited by 8 publications

References 38 publications

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Radiometric Improvement of Spectral Indices Using Multispectral Lightweight Sensors Onboard UAVs

Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification

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