Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
- URL: http://arxiv.org/abs/2411.10924v1
- Date: Sun, 17 Nov 2024 01:02:18 GMT
- Title: Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
- Authors: Priyabrata Karmakar, Manzur Murshed, Shyh Wei Teng,
- Abstract summary: We present a novel approach to grain quality assessment using hyperspectral imaging (HSI) and few-shot learning techniques.
HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information.
We evaluate the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types.
- Score: 2.4797200957733576
- License:
- Abstract: Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to grain quality assessment using HSI combined with few-shot learning (FSL) techniques. Traditional methods for grain quality evaluation, while reliable, are invasive, time-consuming, and costly. HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information. However, a significant challenge in applying DCNNs for HSI-based grain classification is the need for large labelled databases, which are often difficult to obtain. To address this, we explore the use of FSL, which enables models to perform well with limited labelled data, making it a practical solution for real-world applications where rapid deployment is required. We also explored the application of FSL for the classification of hyperspectral images of bulk grains to enable rapid quality assessment at various receival points in the grain supply chain. We evaluated the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types, a crucial feature for real-world applications. In the first scenario, we introduce a novel approach using pre-computed collective class prototypes (CCPs) to enhance inference efficiency and robustness. In the second scenario, we assess the model's ability to classify novel grain types using limited support examples. Our experimental results show that despite using very limited labelled data for training, our FSL classifiers accuracy is comparable to that of a fully trained classifier trained using a significantly larger labelled database.
Related papers
- Refining Tuberculosis Detection in CXR Imaging: Addressing Bias in Deep Neural Networks via Interpretability [1.9936075659851882]
We argue that the reliability of deep learning models is limited, even if they can be shown to obtain perfect classification accuracy on the test data.
We show that pre-training a deep neural network on a large-scale proxy task, as well as using mixed objective optimization network (MOON), can improve the alignment of decision foundations between models and experts.
arXiv Detail & Related papers (2024-07-19T06:41:31Z) - Activate and Reject: Towards Safe Domain Generalization under Category
Shift [71.95548187205736]
We study a practical problem of Domain Generalization under Category Shift (DGCS)
It aims to simultaneously detect unknown-class samples and classify known-class samples in the target domains.
Compared to prior DG works, we face two new challenges: 1) how to learn the concept of unknown'' during training with only source known-class samples, and 2) how to adapt the source-trained model to unseen environments.
arXiv Detail & Related papers (2023-10-07T07:53:12Z) - Universal Semi-Supervised Learning for Medical Image Classification [21.781201758182135]
Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data.
Traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution.
We propose a unified framework to leverage unseen unlabeled data for open-scenario semi-supervised medical image classification.
arXiv Detail & Related papers (2023-04-08T16:12:36Z) - Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for
Open-Set Semi-Supervised Learning [101.28281124670647]
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data.
We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning.
Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
arXiv Detail & Related papers (2021-08-12T09:14:44Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - Identifying nonclassicality from experimental data using artificial
neural networks [52.77024349608834]
We train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions.
We show that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light.
arXiv Detail & Related papers (2021-01-18T15:12:47Z) - Image-based Automated Species Identification: Can Virtual Data
Augmentation Overcome Problems of Insufficient Sampling? [0.0]
We present a two-level data augmentation approach to automated visual species identification.
The first level of data augmentation applies classic approaches of data augmentation and generation of faked images.
The second level of data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space.
arXiv Detail & Related papers (2020-10-18T15:44:45Z) - Fuzziness-based Spatial-Spectral Class Discriminant Information
Preserving Active Learning for Hyperspectral Image Classification [0.456877715768796]
This work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving method.
Experimental results on benchmark HSI datasets demonstrate the effectiveness of the FLG method on Generative, Extreme Learning Machine and Sparse Multinomial Logistic Regression.
arXiv Detail & Related papers (2020-05-28T18:58:11Z) - Semi-supervised Medical Image Classification with Relation-driven
Self-ensembling Model [71.80319052891817]
We present a relation-driven semi-supervised framework for medical image classification.
It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations.
Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
arXiv Detail & Related papers (2020-05-15T06:57:54Z) - Improving Deep Hyperspectral Image Classification Performance with
Spectral Unmixing [3.84448093764973]
We propose an abundance-based multi-HSI classification method.
We convert every HSI from the spectral domain to the abundance domain by a dataset-specific autoencoder.
Secondly, the abundance representations from multiple HSIs are collected to form an enlarged dataset.
arXiv Detail & Related papers (2020-04-01T17:14:05Z) - Adversarial Feature Hallucination Networks for Few-Shot Learning [84.31660118264514]
Adversarial Feature Hallucination Networks (AFHN) is based on conditional Wasserstein Generative Adversarial networks (cWGAN)
Two novel regularizers are incorporated into AFHN to encourage discriminability and diversity of the synthesized features.
arXiv Detail & Related papers (2020-03-30T02:43:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.