Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?
Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to coll...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/fd18c1a6f0454c0ca3a24a360fcbaa59 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:fd18c1a6f0454c0ca3a24a360fcbaa59 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:fd18c1a6f0454c0ca3a24a360fcbaa592021-11-25T16:33:49ZSimulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?10.3390/app1122106282076-3417https://doaj.org/article/fd18c1a6f0454c0ca3a24a360fcbaa592021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10628https://doaj.org/toc/2076-3417Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications.John ChauvinRay DuranKouhyar TavakolianAlireza AkhbardehNicholas MacKinnonJianwei QinDiane E. ChanChansong HwangInsuck BaekMoon S. KimRachel B. IsaacsAyse Gamze YilmazJiahleen RoungchunRosalee S. HellbergFartash VasefiMDPI AGarticleclassificationhyperspectral imagingfood fraudsimulated annealingmachine learningspectroscopyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10628, p 10628 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
classification hyperspectral imaging food fraud simulated annealing machine learning spectroscopy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
classification hyperspectral imaging food fraud simulated annealing machine learning spectroscopy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 John Chauvin Ray Duran Kouhyar Tavakolian Alireza Akhbardeh Nicholas MacKinnon Jianwei Qin Diane E. Chan Chansong Hwang Insuck Baek Moon S. Kim Rachel B. Isaacs Ayse Gamze Yilmaz Jiahleen Roungchun Rosalee S. Hellberg Fartash Vasefi Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| description |
Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications. |
| format |
article |
| author |
John Chauvin Ray Duran Kouhyar Tavakolian Alireza Akhbardeh Nicholas MacKinnon Jianwei Qin Diane E. Chan Chansong Hwang Insuck Baek Moon S. Kim Rachel B. Isaacs Ayse Gamze Yilmaz Jiahleen Roungchun Rosalee S. Hellberg Fartash Vasefi |
| author_facet |
John Chauvin Ray Duran Kouhyar Tavakolian Alireza Akhbardeh Nicholas MacKinnon Jianwei Qin Diane E. Chan Chansong Hwang Insuck Baek Moon S. Kim Rachel B. Isaacs Ayse Gamze Yilmaz Jiahleen Roungchun Rosalee S. Hellberg Fartash Vasefi |
| author_sort |
John Chauvin |
| title |
Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| title_short |
Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| title_full |
Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| title_fullStr |
Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| title_full_unstemmed |
Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? |
| title_sort |
simulated annealing-based hyperspectral data optimization for fish species classification: can the number of measured wavelengths be reduced? |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/fd18c1a6f0454c0ca3a24a360fcbaa59 |
| work_keys_str_mv |
AT johnchauvin simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT rayduran simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT kouhyartavakolian simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT alirezaakhbardeh simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT nicholasmackinnon simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT jianweiqin simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT dianeechan simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT chansonghwang simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT insuckbaek simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT moonskim simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT rachelbisaacs simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT aysegamzeyilmaz simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT jiahleenroungchun simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT rosaleeshellberg simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced AT fartashvasefi simulatedannealingbasedhyperspectraldataoptimizationforfishspeciesclassificationcanthenumberofmeasuredwavelengthsbereduced |
| _version_ |
1718413177164660736 |