Sparse Recovery of Hyperspectral Signal from Natural RGB Images

Arad, Boaz; Ben-Shahar, Ohad

doi:10.1007/978-3-319-46478-7_2

Cited by 452 publications

(493 citation statements)

References 31 publications

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“…Table I briefly reviews the current extent of aerial hyperspectral datasets available for analysis. Other hyperspectral datasets include ICVL (Arad and Ben-Shahar [10]) and CAVE (Yasuma et al [11]) -however, we do not include them in the table as they are non-nadir and do not have pixel-wise labels for the data. The most commonly used aerial datasets are (1) Indian Pines, (2) Salinas Valley, and (3) Univ.…”

Section: A Datasets For Hyperspectral Remote Sensing Imagerymentioning

confidence: 99%

AeroRIT: A New Scene for Hyperspectral Image Analysis

Rangnekar

Mokashi

Ientilucci

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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We investigate modifying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRITthat is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various subcategories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the performance of three popular architectures -SegNet, U-Net, and Res-U-Net, for scene understanding and object identification via the task of dense semantic segmentation to establish a benchmark for the scene. To further strengthen the network, we add squeeze and excitation blocks for better channel interactions and use self-supervised learning for better encoder initialization. Aerial hyperspectral image analysis has been restricted to small datasets with limited train/test splits capabilities and we believe that AeroRIT will help advance the research in the field with a more complex object distribution to perform well on. The full dataset, with flight lines in radiance and reflectance domain, is available for download at https://github.com/aneesh3108/AeroRIT. This dataset is the first step towards developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks like vehicle tracking and occlusion handling.

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Section: A Datasets For Hyperspectral Remote Sensing Imagerymentioning

confidence: 99%

AeroRIT: A New Scene for Hyperspectral Image Analysis

Rangnekar

Mokashi

Ientilucci

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…Ours [18] with a single pair [5] with a single pair (e) RMSE comparison for each patch of the colorchart Figure 8. Spectral reflectance estimation results on the 24 patches of the colorchart.…”

Section: Rmsementioning

confidence: 99%

“…existing single-view image-based methods [18,5] (average withing each patch) only using the projector-camera pair 4. Figure 8(e) shows the corresponding RMSE comparison for each patch, where we can confirm that our method achieves much lower RMSE than the existing methods.…”

Section: Rmsementioning

confidence: 99%

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Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance From Motion

Monno

Hidaka

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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In this paper, we propose a novel projector-camera system for practical and low-cost acquisition of a dense object 3D model with the spectral reflectance property. In our system, we use a standard RGB camera and leverage an offthe-shelf projector as active illumination for both the 3D reconstruction and the spectral reflectance estimation. We first reconstruct the 3D points while estimating the poses of the camera and the projector, which are alternately moved around the object, by combining multi-view structured light and structure-from-motion (SfM) techniques. We then exploit the projector for multispectral imaging and estimate the spectral reflectance of each 3D point based on a novel spectral reflectance estimation model considering the geometric relationship between the reconstructed 3D points and the estimated projector positions. Experimental results on several real objects demonstrate that our system can precisely acquire a dense 3D model with the full spectral reflectance property using off-the-shelf devices.

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“…RGB or YCbCr images), hyperspectral imaging systems enable researchers the opportunity to capture data from the observed scenes with high spatial and redundant spectral resolution (both visible and non-visible spectrum to human eye) of the observed scenes from the radiance or reflected light source from objects [2,3,4,5]. These data have been used in many applications (remote sensing [2,6,9], scene analysis or object detection [2,6,7,8,9,10,11], spectral estimation [11,12,13,14], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Salient Object Detection on Hyperspectral Images Using Features Learned from Unsupervised Segmentation Task

İmamoğlu

Ding

Fang

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyperspectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image segmentation task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.

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Sparse Recovery of Hyperspectral Signal from Natural RGB Images

Cited by 452 publications

References 31 publications

AeroRIT: A New Scene for Hyperspectral Image Analysis

AeroRIT: A New Scene for Hyperspectral Image Analysis

Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance From Motion

Salient Object Detection on Hyperspectral Images Using Features Learned from Unsupervised Segmentation Task

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