78 research outputs found
Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products
Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images.info:eu-repo/semantics/acceptedVersio
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Hyperspectral imaging acquires data in both the spatial and frequency domains
to offer abundant physical or biological information. However, conventional
hyperspectral imaging has intrinsic limitations of bulky instruments, slow data
acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral
learning for snapshot hyperspectral imaging in which sampled hyperspectral data
in a small subarea are incorporated into a learning algorithm to recover the
hypercube. Hyperspectral learning exploits the idea that a photograph is more
than merely a picture and contains detailed spectral information. A small
sampling of hyperspectral data enables spectrally informed learning to recover
a hypercube from an RGB image. Hyperspectral learning is capable of recovering
full spectroscopic resolution in the hypercube, comparable to high spectral
resolutions of scientific spectrometers. Hyperspectral learning also enables
ultrafast dynamic imaging, leveraging ultraslow video recording in an
off-the-shelf smartphone, given that a video comprises a time series of
multiple RGB images. To demonstrate its versatility, an experimental model of
vascular development is used to extract hemodynamic parameters via statistical
and deep-learning approaches. Subsequently, the hemodynamics of peripheral
microcirculation is assessed at an ultrafast temporal resolution up to a
millisecond, using a conventional smartphone camera. This spectrally informed
learning method is analogous to compressed sensing; however, it further allows
for reliable hypercube recovery and key feature extractions with a transparent
learning algorithm. This learning-powered snapshot hyperspectral imaging method
yields high spectral and temporal resolutions and eliminates the spatiospectral
tradeoff, offering simple hardware requirements and potential applications of
various machine-learning techniques.Comment: This paper will appear in PNAS Nexu
Illumination Invariant Deep Learning for Hyperspectral Data
Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination invariant feature representations and classification models for hyperspectral data captured outdoors, under natural sunlight. The approaches integrate domain knowledge into learning algorithms and hence does not rely on a priori knowledge of atmospheric parameters, additional sensors or large amounts of labelled training data. Hyperspectral sensors record rich semantic information from a scene, making them useful for robotics or remote sensing applications where perception systems are used to gain an understanding of the scene. Images recorded by hyperspectral sensors can, however, be affected to varying degrees by intrinsic factors relating to the sensor itself (keystone, smile, noise, particularly at the limits of the sensed spectral range) but also by extrinsic factors such as the way the scene is illuminated. The appearance of the scene in the image is tied to the incident illumination which is dependent on variables such as the position of the sun, geometry of the surface and the prevailing atmospheric conditions. Effects like shadows can make the appearance and spectral characteristics of identical materials to be significantly different. This degrades the performance of high-level algorithms that use hyperspectral data, such as those that do classification and clustering. If sufficient training data is available, learning algorithms such as neural networks can capture variability in the scene appearance and be trained to compensate for it. Learning algorithms are advantageous for this task because they do not require a priori knowledge of the prevailing atmospheric conditions or data from additional sensors. Labelling of hyperspectral data is, however, difficult and time-consuming, so acquiring enough labelled samples for the learning algorithm to adequately capture the scene appearance is challenging. Hence, there is a need for the development of techniques that are invariant to the effects of illumination that do not require large amounts of labelled data. In this thesis, an approach to learning a representation of hyperspectral data that is invariant to the effects of illumination is proposed. This approach combines a physics-based model of the illumination process with an unsupervised deep learning algorithm, and thus requires no labelled data. Datasets that vary both temporally and spatially are used to compare the proposed approach to other similar state-of-the-art techniques. The results show that the learnt representation is more invariant to shadows in the image and to variations in brightness due to changes in the scene topography or position of the sun in the sky. The results also show that a supervised classifier can predict class labels more accurately and more consistently across time when images are represented using the proposed method. Additionally, this thesis proposes methods to train supervised classification models to be more robust to variations in illumination where only limited amounts of labelled data are available. The transfer of knowledge from well-labelled datasets to poorly labelled datasets for classification is investigated. A method is also proposed for enabling small amounts of labelled samples to capture the variability in spectra across the scene. These samples are then used to train a classifier to be robust to the variability in the data caused by variations in illumination. The results show that these approaches make convolutional neural network classifiers more robust and achieve better performance when there is limited labelled training data. A case study is presented where a pipeline is proposed that incorporates the methods proposed in this thesis for learning robust feature representations and classification models. A scene is clustered using no labelled data. The results show that the pipeline groups the data into clusters that are consistent with the spatial distribution of the classes in the scene as determined from ground truth
Intrinsic Image Transfer for Illumination Manipulation
This paper presents a novel intrinsic image transfer (IIT) algorithm for
illumination manipulation, which creates a local image translation between two
illumination surfaces. This model is built on an optimization-based framework
consisting of three photo-realistic losses defined on the sub-layers factorized
by an intrinsic image decomposition. We illustrate that all losses can be
reduced without the necessity of taking an intrinsic image decomposition under
the well-known spatial-varying illumination illumination-invariant reflectance
prior knowledge. Moreover, with a series of relaxations, all of them can be
directly defined on images, giving a closed-form solution for image
illumination manipulation. This new paradigm differs from the prevailing
Retinex-based algorithms, as it provides an implicit way to deal with the
per-pixel image illumination. We finally demonstrate its versatility and
benefits to the illumination-related tasks such as illumination compensation,
image enhancement, and high dynamic range (HDR) image compression, and show the
high-quality results on natural image datasets
Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data
Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC)
types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels
into constituent LULC types and their abundance fractions. While existing
studies on Deep Learning (DL) for SU typically focus on single time-step
hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS
MS time series, addressing missing data with end-to-end DL models. Our approach
enhances a Long-Short Term Memory (LSTM)-based model by incorporating
geographic, topographic (geo-topographic), and climatic ancillary information.
Notably, our method eliminates the need for explicit endmember extraction,
instead learning the input-output relationship between mixed spectra and LULC
abundances through supervised learning. Experimental results demonstrate that
integrating spectral-temporal input data with geo-topographic and climatic
information significantly improves the estimation of LULC abundances in mixed
pixels. To facilitate this study, we curated a novel labeled dataset for
Andalusia (Spain) with monthly MODIS multispectral time series at 460m
resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing
(Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC
abundances along with ancillary information. The dataset
(https://zenodo.org/records/7752348) and code
(https://github.com/jrodriguezortega/MSMTU) are available to the public
PATTERN RECOGNITION INTEGRATED SENSING METHODOLOGIES (PRISMS) IN PHARMACEUTICAL PROCESS VALIDATION, REMOTE SENSING AND ASTROBIOLOGY
Modern analytical instrumentation is capable of creating enormous and complex volumes of data. Analysis of large data volumes are complicated by lengthy analysis time and high computational demand. Incorporating real-time analysis methods that are computationally efficient are desirable for modern analytical methods to be fully utilized. The use of modern instrumentation in on-line pharmaceutical process validation, remote sensing, and astrobiology applications requires real-time analysis methods that are computationally efficient.
Integrated sensing and processing (ISP) is a method for minimizing the data burden and sensing time of a system. ISP is accomplished through implementation of chemometric calculations in the physics of the spectroscopic sensor itself. In ISP, the measurements collected at the detector are weighted to directly correlate to the sample properties of interest. This method is especially useful for large and complex data sets. In this research, ISP is applied to acoustic resonance spectroscopy, near-infrared hyperspectral imaging and a novel solid state spectral imager. In each application ISP produced a clear advantage over the traditional sensing method.
The limitations of ISP must be addressed before it can become widely used. ISP is essentially a pattern recognition algorithm. Problems arise in pattern recognition when the pattern-recognition algorithm encounters a sample unlike any in the original calibration set. This is termed the false sample problem. To address the false sample problem the Bootstrap Error-Adjusted Single-Sample Technique (BEST, a nonparametric classification technique) was investigated. The BEST-ISP method utilizes a hashtable of normalized BEST points along an asymmetric probability density contour to estimate the BEST multidimensional standard deviation of a sample. The on-line application of the BEST method requires significantly less computation than the full algorithm allowing it to be utilized in real time as sample data is obtained. This research tests the hypothesis that a BEST-ISP metric can be used to detect false samples with sensitivity \u3e 90% and specificity \u3e 90% on categorical data
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
Multispectral Image Road Extraction Based Upon Automated Map Conflation
Road network extraction from remotely sensed imagery enables many important and diverse applications such as vehicle tracking, drone navigation, and intelligent transportation studies. There are, however, a number of challenges to road detection from an image. Road pavement material, width, direction, and topology vary across a scene. Complete or partial occlusions caused by nearby buildings, trees, and the shadows cast by them, make maintaining road connectivity difficult. The problems posed by occlusions are exacerbated with the increasing use of oblique imagery from aerial and satellite platforms. Further, common objects such as rooftops and parking lots are made of materials similar or identical to road pavements. This problem of common materials is a classic case of a single land cover material existing for different land use scenarios.
This work addresses these problems in road extraction from geo-referenced imagery by leveraging the OpenStreetMap digital road map to guide image-based road extraction. The crowd-sourced cartography has the advantages of worldwide coverage that is constantly updated. The derived road vectors follow only roads and so can serve to guide image-based road extraction with minimal confusion from occlusions and changes in road material. On the other hand, the vector road map has no information on road widths and misalignments between the vector map and the geo-referenced image are small but nonsystematic. Properly correcting misalignment between two geospatial datasets, also known as map conflation, is an essential step.
A generic framework requiring minimal human intervention is described for multispectral image road extraction and automatic road map conflation. The approach relies on the road feature generation of a binary mask and a corresponding curvilinear image. A method for generating the binary road mask from the image by applying a spectral measure is presented. The spectral measure, called anisotropy-tunable distance (ATD), differs from conventional measures and is created to account for both changes of spectral direction and spectral magnitude in a unified fashion. The ATD measure is particularly suitable for differentiating urban targets such as roads and building rooftops. The curvilinear image provides estimates of the width and orientation of potential road segments. Road vectors derived from OpenStreetMap are then conflated to image road features by applying junction matching and intermediate point matching, followed by refinement with mean-shift clustering and morphological processing to produce a road mask with piecewise width estimates.
The proposed approach is tested on a set of challenging, large, and diverse image data sets and the performance accuracy is assessed. The method is effective for road detection and width estimation of roads, even in challenging scenarios when extensive occlusion occurs
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