1,385 research outputs found
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images
High-quality labeled datasets are essential for deep learning. Traditional
manual annotation methods are not only costly and inefficient but also pose
challenges in specialized domains where expert knowledge is needed.
Self-supervised methods, despite leveraging unlabeled data for feature
extraction, still require hundreds or thousands of labeled instances to guide
the model for effective specialized image classification. Current unsupervised
learning methods offer automatic classification without prior annotation but
often compromise on accuracy. As a result, efficiently procuring high-quality
labeled datasets remains a pressing challenge for specialized domain images
devoid of annotated data. Addressing this, an unsupervised classification
method with three key ideas is introduced: 1) dual-step feature dimensionality
reduction using a pre-trained model and manifold learning, 2) a voting
mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior
manual annotation. This approach outperforms supervised methods in
classification accuracy, as demonstrated with fungal image data, achieving
94.1% and 96.7% on public and private datasets respectively. The proposed
unsupervised classification method reduces dependency on pre-annotated
datasets, enabling a closed-loop for data classification. The simplicity and
ease of use of this method will also bring convenience to researchers in
various fields in building datasets, promoting AI applications for images in
specialized domains
๋งค๊ฐ๋ถํฌ๊ทผ์ฌ๋ฅผ ํตํ ๊ณต์ ์์คํ ๊ณตํ์์์ ํ๋ฅ ๊ธฐ๊ณํ์ต ์ ๊ทผ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ํํ์๋ฌผ๊ณตํ๋ถ, 2021.8. ์ด์ข
๋ฏผ.With the rapid development of measurement technology, higher quality and vast amounts of process data become available. Nevertheless, process data are โscarceโ in many cases as they are sampled only at certain operating conditions while the dimensionality of the system is large. Furthermore, the process data are inherently stochastic due to the internal characteristics of the system or the measurement noises. For this reason, uncertainty is inevitable in process systems, and estimating it becomes a crucial part of engineering tasks as the prediction errors can lead to misguided decisions and cause severe casualties or economic losses. A popular approach to this is applying probabilistic inference techniques that can model the uncertainty in terms of probability. However, most of the existing probabilistic inference techniques are based on recursive sampling, which makes it difficult to use them for industrial applications that require processing a high-dimensional and massive amount of data. To address such an issue, this thesis proposes probabilistic machine learning approaches based on parametric distribution approximation, which can model the uncertainty of the system and circumvent the computational complexity as well. The proposed approach is applied for three major process engineering tasks: process monitoring, system modeling, and process design.
First, a process monitoring framework is proposed that utilizes a probabilistic classifier for fault classification. To enhance the accuracy of the classifier and reduce the computational cost for its training, a feature extraction method called probabilistic manifold learning is developed and applied to the process data ahead of the fault classification. We demonstrate that this manifold approximation process not only reduces the dimensionality of the data but also casts the data into a clustered structure, making the classifier have a low dependency on the type and dimension of the data. By exploiting this property, non-metric information (e.g., fault labels) of the data is effectively incorporated and the diagnosis performance is drastically improved.
Second, a probabilistic modeling approach based on Bayesian neural networks is proposed. The parameters of deep neural networks are transformed into Gaussian distributions and trained using variational inference. The redundancy of the parameter is autonomously inferred during the model training, and insignificant parameters are eliminated a posteriori. Through a verification study, we demonstrate that the proposed approach can not only produce high-fidelity models that describe the stochastic behaviors of the system but also produce the optimal model structure.
Finally, a novel process design framework is proposed based on reinforcement learning. Unlike the conventional optimization methods that recursively evaluate the objective function to find an optimal value, the proposed method approximates the objective function surface by parametric probabilistic distributions. This allows learning the continuous action policy without introducing any cumbersome discretization process. Moreover, the probabilistic policy gives means for effective control of the exploration and exploitation rates according to the certainty information. We demonstrate that the proposed framework can learn process design heuristics during the solution process and use them to solve similar design problems.๊ณ์ธก๊ธฐ์ ์ ๋ฐ๋ฌ๋ก ์์ง์, ๊ทธ๋ฆฌ๊ณ ๋ฐฉ๋ํ ์์ ๊ณต์ ๋ฐ์ดํฐ์ ์ทจ๋์ด ๊ฐ๋ฅํด์ก๋ค. ๊ทธ๋ฌ๋ ๋ง์ ๊ฒฝ์ฐ ์์คํ
์ฐจ์์ ํฌ๊ธฐ์ ๋นํด์ ์ผ๋ถ ์ด์ ์กฐ๊ฑด์ ๊ณต์ ๋ฐ์ดํฐ๋ง์ด ์ทจ๋๋๊ธฐ ๋๋ฌธ์, ๊ณต์ ๋ฐ์ดํฐ๋ โํฌ์โํ๊ฒ ๋๋ค. ๋ฟ๋ง ์๋๋ผ, ๊ณต์ ๋ฐ์ดํฐ๋ ์์คํ
๊ฑฐ๋ ์์ฒด์ ๋๋ถ์ด ๊ณ์ธก์์ ๋ฐ์ํ๋ ๋
ธ์ด์ฆ๋ก ์ธํ ๋ณธ์ง์ ์ธ ํ๋ฅ ์ ๊ฑฐ๋์ ๋ณด์ธ๋ค. ๋ฐ๋ผ์ ์์คํ
์ ์์ธก๋ชจ๋ธ์ ์์ธก ๊ฐ์ ๋ํ ๋ถํ์ค์ฑ์ ์ ๋์ ์ผ๋ก ๊ธฐ์ ํ๋ ๊ฒ์ด ์๊ตฌ๋๋ฉฐ, ์ด๋ฅผ ํตํด ์ค์ง์ ์๋ฐฉํ๊ณ ์ ์ฌ์ ์ธ๋ช
ํผํด์ ๊ฒฝ์ ์ ์์ค์ ๋ฐฉ์งํ ์ ์๋ค. ์ด์ ๋ํ ๋ณดํธ์ ์ธ ์ ๊ทผ๋ฒ์ ํ๋ฅ ์ถ์ ๊ธฐ๋ฒ์ ์ฌ์ฉํ์ฌ ์ด๋ฌํ ๋ถํ์ค์ฑ์ ์ ๋ํ ํ๋ ๊ฒ์ด๋, ํ์กดํ๋ ์ถ์ ๊ธฐ๋ฒ๋ค์ ์ฌ๊ท์ ์ํ๋ง์ ์์กดํ๋ ํน์ฑ์ ๊ณ ์ฐจ์์ด๋ฉด์๋ ๋ค๋์ธ ๊ณต์ ๋ฐ์ดํฐ์ ์ ์ฉํ๊ธฐ ์ด๋ ต๋ค๋ ๊ทผ๋ณธ์ ์ธ ํ๊ณ๋ฅผ ๊ฐ์ง๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์์๋ ๋งค๊ฐ๋ถํฌ๊ทผ์ฌ์ ๊ธฐ๋ฐํ ํ๋ฅ ๊ธฐ๊ณํ์ต์ ์ ์ฉํ์ฌ ์์คํ
์ ๋ด์ฌ๋ ๋ถํ์ค์ฑ์ ๋ชจ๋ธ๋งํ๋ฉด์๋ ๋์์ ๊ณ์ฐ ํจ์จ์ ์ธ ์ ๊ทผ ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค.
๋จผ์ , ๊ณต์ ์ ๋ชจ๋ํฐ๋ง์ ์์ด ๊ฐ์ฐ์์ ํผํฉ ๋ชจ๋ธ (Gaussian mixture model)์ ๋ถ๋ฅ์๋ก ์ฌ์ฉํ๋ ํ๋ฅ ์ ๊ฒฐํจ ๋ถ๋ฅ ํ๋ ์์ํฌ๊ฐ ์ ์๋์๋ค. ์ด๋ ๋ถ๋ฅ์์ ํ์ต์์์ ๊ณ์ฐ ๋ณต์ก๋๋ฅผ ์ค์ด๊ธฐ ์ํ์ฌ ๋ฐ์ดํฐ๋ฅผ ์ ์ฐจ์์ผ๋ก ํฌ์์ํค๋๋ฐ, ์ด๋ฅผ ์ํ ํ๋ฅ ์ ๋ค์์ฒด ํ์ต (probabilistic manifold learn-ing) ๋ฐฉ๋ฒ์ด ์ ์๋์๋ค. ์ ์ํ๋ ๋ฐฉ๋ฒ์ ๋ฐ์ดํฐ์ ๋ค์์ฒด (manifold)๋ฅผ ๊ทผ์ฌํ์ฌ ๋ฐ์ดํฐ ํฌ์ธํธ ์ฌ์ด์ ์๋ณ ์ฐ๋ (pairwise likelihood)๋ฅผ ๋ณด์กดํ๋ ํฌ์๋ฒ์ด ์ฌ์ฉ๋๋ค. ์ด๋ฅผ ํตํ์ฌ ๋ฐ์ดํฐ์ ์ข
๋ฅ์ ์ฐจ์์ ์์กด๋๊ฐ ๋ฎ์ ์ง๋จ ๊ฒฐ๊ณผ๋ฅผ ์ป์๊ณผ ๋์์ ๋ฐ์ดํฐ ๋ ์ด๋ธ๊ณผ ๊ฐ์ ๋น๊ฑฐ๋ฆฌ์ (non-metric) ์ ๋ณด๋ฅผ ํจ์จ์ ์ผ๋ก ์ฌ์ฉํ์ฌ ๊ฒฐํจ ์ง๋จ ๋ฅ๋ ฅ์ ํฅ์์ํฌ ์ ์์์ ๋ณด์๋ค.
๋์งธ๋ก, ๋ฒ ์ด์ง์ ์ฌ์ธต ์ ๊ฒฝ๋ง(Bayesian deep neural networks)์ ์ฌ์ฉํ ๊ณต์ ์ ํ๋ฅ ์ ๋ชจ๋ธ๋ง ๋ฐฉ๋ฒ๋ก ์ด ์ ์๋์๋ค. ์ ๊ฒฝ๋ง์ ๊ฐ ๋งค๊ฐ๋ณ์๋ ๊ฐ์ฐ์ค ๋ถํฌ๋ก ์นํ๋๋ฉฐ, ๋ณ๋ถ์ถ๋ก (variational inference)์ ํตํ์ฌ ๊ณ์ฐ ํจ์จ์ ์ธ ํ๋ จ์ด ์งํ๋๋ค. ํ๋ จ์ด ๋๋ ํ ํ๋ผ๋ฏธํฐ์ ์ ํจ์ฑ์ ์ธก์ ํ์ฌ ๋ถํ์ํ ๋งค๊ฐ๋ณ์๋ฅผ ์๊ฑฐํ๋ ์ฌํ ๋ชจ๋ธ ์์ถ ๋ฐฉ๋ฒ์ด ์ฌ์ฉ๋์๋ค. ๋ฐ๋์ฒด ๊ณต์ ์ ๋ํ ์ฌ๋ก ์ฐ๊ตฌ๋ ์ ์ํ๋ ๋ฐฉ๋ฒ์ด ๊ณต์ ์ ๋ณต์กํ ๊ฑฐ๋์ ํจ๊ณผ์ ์ผ๋ก ๋ชจ๋ธ๋ง ํ ๋ฟ๋ง ์๋๋ผ ๋ชจ๋ธ์ ์ต์ ๊ตฌ์กฐ๋ฅผ ๋์ถํ ์ ์์์ ๋ณด์ฌ์ค๋ค.
๋ง์ง๋ง์ผ๋ก, ๋ถํฌํ ์ฌ์ธต ์ ๊ฒฝ๋ง์ ์ฌ์ฉํ ๊ฐํํ์ต์ ๊ธฐ๋ฐ์ผ๋ก ํ ํ๋ฅ ์ ๊ณต์ ์ค๊ณ ํ๋ ์์ํฌ๊ฐ ์ ์๋์๋ค. ์ต์ ์น๋ฅผ ์ฐพ๊ธฐ ์ํด ์ฌ๊ท์ ์ผ๋ก ๋ชฉ์ ํจ์ ๊ฐ์ ํ๊ฐํ๋ ๊ธฐ์กด์ ์ต์ ํ ๋ฐฉ๋ฒ๋ก ๊ณผ ๋ฌ๋ฆฌ, ๋ชฉ์ ํจ์ ๊ณก๋ฉด (objective function surface)์ ๋งค๊ฐํ ๋ ํ๋ฅ ๋ถํฌ๋ก ๊ทผ์ฌํ๋ ์ ๊ทผ๋ฒ์ด ์ ์๋์๋ค. ์ด๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ด์ฐํ (discretization)๋ฅผ ์ฌ์ฉํ์ง ์๊ณ ์ฐ์์ ํ๋ ์ ์ฑ
์ ํ์ตํ๋ฉฐ, ํ์ค์ฑ (certainty)์ ๊ธฐ๋ฐํ ํ์ (exploration) ๋ฐ ํ์ฉ (exploi-tation) ๋น์จ์ ์ ์ด๊ฐ ํจ์จ์ ์ผ๋ก ์ด๋ฃจ์ด์ง๋ค. ์ฌ๋ก ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ ๊ณต์ ์ ์ค๊ณ์ ๋ํ ๊ฒฝํ์ง์ (heuristic)์ ํ์ตํ๊ณ ์ ์ฌํ ์ค๊ณ ๋ฌธ์ ์ ํด๋ฅผ ๊ตฌํ๋ ๋ฐ ์ด์ฉํ ์ ์์์ ๋ณด์ฌ์ค๋ค.Chapter 1 Introduction 1
1.1. Motivation 1
1.2. Outline of the thesis 5
Chapter 2 Backgrounds and preliminaries 9
2.1. Bayesian inference 9
2.2. Monte Carlo 10
2.3. Kullback-Leibler divergence 11
2.4. Variational inference 12
2.5. Riemannian manifold 13
2.6. Finite extended-pseudo-metric space 16
2.7. Reinforcement learning 16
2.8. Directed graph 19
Chapter 3 Process monitoring and fault classification with probabilistic manifold learning 20
3.1. Introduction 20
3.2. Methods 25
3.2.1. Uniform manifold approximation 27
3.2.2. Clusterization 28
3.2.3. Projection 31
3.2.4. Mapping of unknown data query 32
3.2.5. Inference 33
3.3. Verification study 38
3.3.1. Dataset description 38
3.3.2. Experimental setup 40
3.3.3. Process monitoring 43
3.3.4. Projection characteristics 47
3.3.5. Fault diagnosis 50
3.3.6. Computational Aspects 56
Chapter 4 Process system modeling with Bayesian neural networks 59
4.1. Introduction 59
4.2. Methods 63
4.2.1. Long Short-Term Memory (LSTM) 63
4.2.2. Bayesian LSTM (BLSTM) 66
4.3. Verification study 68
4.3.1. System description 68
4.3.2. Estimation of the plasma variables 71
4.3.3. Dataset description 72
4.3.4. Experimental setup 72
4.3.5. Weight regularization during training 78
4.3.6. Modeling complex behaviors of the system 80
4.3.7. Uncertainty quantification and model compression 85
Chapter 5 Process design based on reinforcement learning with distributional actor-critic networks 89
5.1. Introduction 89
5.2. Methods 93
5.2.1. Flowsheet hashing 93
5.2.2. Behavioral cloning 99
5.2.3. Neural Monte Carlo tree search (N-MCTS) 100
5.2.4. Distributional actor-critic networks (DACN) 105
5.2.5. Action masking 110
5.3. Verification study 110
5.3.1. System description 110
5.3.2. Experimental setup 111
5.3.3. Result and discussions 115
Chapter 6 Concluding remarks 120
6.1. Summary of the contributions 120
6.2. Future works 122
Appendix 125
A.1. Proof of Lemma 1 125
A.2. Performance indices for dimension reduction 127
A.3. Model equations for process units 130
Bibliography 132
์ด ๋ก 149๋ฐ
Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies
Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms
Multi-Source Data Fusion for Cyberattack Detection in Power Systems
Cyberattacks can cause a severe impact on power systems unless detected
early. However, accurate and timely detection in critical infrastructure
systems presents challenges, e.g., due to zero-day vulnerability exploitations
and the cyber-physical nature of the system coupled with the need for high
reliability and resilience of the physical system. Conventional rule-based and
anomaly-based intrusion detection system (IDS) tools are insufficient for
detecting zero-day cyber intrusions in the industrial control system (ICS)
networks. Hence, in this work, we show that fusing information from multiple
data sources can help identify cyber-induced incidents and reduce false
positives. Specifically, we present how to recognize and address the barriers
that can prevent the accurate use of multiple data sources for fusion-based
detection. We perform multi-source data fusion for training IDS in a
cyber-physical power system testbed where we collect cyber and physical side
data from multiple sensors emulating real-world data sources that would be
found in a utility and synthesizes these into features for algorithms to detect
intrusions. Results are presented using the proposed data fusion application to
infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks.
Post collection, the data fusion application uses time-synchronized merge and
extracts features followed by pre-processing such as imputation and encoding
before training supervised, semi-supervised, and unsupervised learning models
to evaluate the performance of the IDS. A major finding is the improvement of
detection accuracy by fusion of features from cyber, security, and physical
domains. Additionally, we observed the co-training technique performs at par
with supervised learning methods when fed with our features
Exploiting geometry, topology, and optimization for knowledge discovery in big data
2013 Summer.Includes bibliographical references.In this dissertation, we consider several topics that are united by the theme of topological and geometric data analysis. First, we consider an application in landscape ecology using a well-known vector quantization algorithm to characterize and segment the color content of natural imagery. Color information in an image may be viewed naturally as clusters of pixels with similar attributes. The inherent structure and distribution of these clusters serves to quantize the information in the image and provides a basis for classification. A friendly graphical user interface called Biological Landscape Organizer and Semi-supervised Segmenting Machine (BLOSSM) was developed to aid in this classification. We consider four different choices for color space and five different metrics in which to analyze our data, and results are compared. Second, we present a novel topologically driven clustering algorithm that blends Locally Linear Embedding (LLE) and vector quantization by mapping color information to a lower dimensional space, identifying distinct color regions, and classifying pixels together based on both a proximity measure and color content. It is observed that these techniques permit a significant reduction in color resolution while maintaining the visually important features of images. Third, we develop a novel algorithm which we call Sparse LLE that leads to sparse representations in local reconstructions by using a data weighted 1-norm regularization term in the objective function of an optimization problem. It is observed that this new formulation has proven effective at automatically determining an appropriate number of nearest neighbors for each data point. We explore various optimization techniques, namely Primal Dual Interior Point algorithms, to solve this problem, comparing the computational complexity for each. Fourth, we present a novel algorithm that can be used to determine the boundary of a data set, or the vertices of a convex hull encasing a point cloud of data, in any dimension by solving a quadratic optimization problem. In this problem, each point is written as a linear combination of its nearest neighbors where the coefficients of this linear combination are penalized if they do not construct a convex combination, revealing those points that cannot be represented in this way, the vertices of the convex hull containing the data. Finally, we exploit the relatively new tool from topological data analysis, persistent homology, and consider the use of vector bundles to re-embed data in order to improve the topological signal of a data set by embedding points sampled from a projective variety into successive Grassmannians
Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture
This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
Hyperspectral imaging (HSI) is a non-destructive and contactless technology
that provides valuable information about the structure and composition of an
object. It can capture detailed information about the chemical and physical
properties of agricultural crops. Due to its wide spectral range, compared with
multispectral- or RGB-based imaging methods, HSI can be a more effective tool
for monitoring crop health and productivity. With the advent of this imaging
tool in agrotechnology, researchers can more accurately address issues related
to the detection of diseased and defective crops in the agriculture industry.
This allows to implement the most suitable and accurate farming solutions, such
as irrigation and fertilization before crops enter a damaged and
difficult-to-recover phase of growth in the field. While HSI provides valuable
insights into the object under investigation, the limited number of HSI
datasets for crop evaluation presently poses a bottleneck. Dealing with the
curse of dimensionality presents another challenge due to the abundance of
spectral and spatial information in each hyperspectral cube. State-of-the-art
methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and
spatial information. On the other hand, 3D-CNN-based models have shown
significant promise in achieving better classification and detection results by
leveraging spectral and spatial features simultaneously. Despite the apparent
benefits of 3D-CNN-based models, their usage for classification purposes in
this area of research has remained limited. This paper seeks to address this
gap by reviewing 3D-CNN-based architectures and the typical deep learning
pipeline, including preprocessing and visualization of results, for the
classification of hyperspectral images of diseased and defective crops.
Furthermore, we discuss open research areas and challenges when utilizing
3D-CNNs with HSI data
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