Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.

show abstract

“…Intermediate maps are created after the filtering process. h i = h((x+stepSize), t) 8: if h x < h d then 9:…”

Section: Filteringmentioning

confidence: 99%

“…Some algorithms use band indices to detect shadows in an HSI image [6,7]. Another class of algorithms require an a priori Digital Surface Model (DSM) [8] or Terrestrial Laser Scanning data together with the HSI image to find shadows cast from scene geometry [9].…”

Section: Introductionmentioning

confidence: 99%

A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

Zahidi

Chatterjee

Yuen

2019

MAKE

View full text Add to dashboard Cite

show abstract

“…A number of authors have considered fusion of hyperspectral and LiDAR data to address various problems that include registering hyperspectral data to LiDAR data to improve geological interpretation and accuracy [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], illumination correction [ 20 , 21 ], assessing slope stability [ 22 ], and to localise data in airborne applications [ 23 , 24 ]. In this paper, LiDAR in combination with GNSS is used to spatially locate hyperspectral pixels within the surveyed scene.…”

Section: Introductionmentioning

confidence: 99%

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

Choros

Job

Edgar³

et al. 2022

Sensors

View full text Add to dashboard Cite

This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.

show abstract

“…Specifically, HSIs are prone to include information redun-dancy as a result of high correlation and the Hughes phenomenon caused by a high spectral dimension [7,8]. In addition, environmental factors, such as clouds and noise, will also cause information confusion when the remote sensor captures scene data [9]. Compared with HSI, LiDAR integrates a laser ranging system, a global positioning system, and an inertial navigation system, so that it can collect the position and intensity information of objects in a threedimensional space [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

Jia

Zhan

2021

J Remote Sens

View full text Add to dashboard Cite

The joint interpretation of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data has developed rapidly in recent years due to continuously evolving image processing technology. Nowadays, most feature extraction methods are carried out by convolving the raw data with fixed-size filters, whereas the structural and texture information of objects in multiple scales cannot be sufficiently exploited. In this article, a shearlet-based structure-aware filtering approach, abbreviated as ShearSAF, is proposed for HSI and LiDAR feature extraction and classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is firstly adopted on raw HSIs to reduce the dimensions. Then, the KPCA-reduced HSI and LiDAR data are converted to the shearlet domain for texture and area feature extraction. In contrast, superpixel segmentation algorithm utilizes the raw HSI data to obtain the initial oversegmentation map. Subsequently, by utilizing a well-designed minimum merging cost that fully considers spectral (HSI and LiDAR data), texture, and area features, a region merging procedure is gradually conducted to produce a final merging map. Further, a scale map that locally indicates the filter size is achieved by calculating the edge distance. Finally, the KPCA-reduced HSI and LiDAR data are convolved with the locally adaptive filters for feature extraction, and a random forest (RF) classifier is thus adopted for classification. The effectiveness of our ShearSAF approach is verified on three real-world datasets, and the results show that the performance of ShearSAF can achieve an accuracy higher than that of comparison methods when exploiting small-size training sample problems. The codes of this work will be available at http://jiasen.tech/papers/ for the sake of reproducibility.

show abstract

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

Cited by 15 publications

References 36 publications

A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

Contact Info

Product

Resources

About