Hyperspectral image segmentation: a comprehensive survey

Grewal, Reaya; Kasana, Singara Singh; Kasana, Geeta

doi:10.1007/s11042-022-13959-w

Cited by 13 publications

(6 citation statements)

References 97 publications

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“…The pixels in the mask were then defined as ROIs, and the spectral data of these ROIs were extracted from the calibrated hyperspectral images. Through this segmentation technique, images were divided into various ROIs with similar properties, including nonredundant features that provide meaningful data …”

Section: Methodsmentioning

confidence: 99%

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Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

Sun,

Nayani,

et al. 2024

ACS Food Sci. Technol.

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Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky–Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model’s predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry.

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Section: Methodsmentioning

confidence: 99%

“…Through this segmentation technique, images were divided into various ROIs with similar properties, including nonredundant features that provide meaningful data. 30 2.6. Data Modeling.…”

Section: Roi Identificationmentioning

confidence: 99%

Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

Sun,

Nayani,

et al. 2024

ACS Food Sci. Technol.

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“…Therefore, an elite mutation that executes swiftly is put forward. Only at finish for every iteration, sort individual fitness values, select an elite sparrow with the best fitness value, and update its position according to (4).…”

Section: Elite Mutationmentioning

confidence: 99%

“…Common methods are: Thresholding, Region merging and split, Clustering, and Semantic segmentation. They employ distinct color blocks to differentiate locations based on image discontinuities, color or grayscale similarity, texture, and other characteristics [4]. The Region-growing [5] is typically effective at segmenting smooth areas of aggregate images, and yet the algorithm suffers from severe under-segmentation when aggregate particles are adhesion.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-class Segmentation Model of Aggregate Image Based on Improved Sparrow Search Algorithm

2023

KSII TIIS

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Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

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“…In recent years, hyperspectral detection technology [8][9][10][11][12][13] has achieved good results in target segmentation and quality detection tasks by simultaneously using image information and spectral information from seeds. For example, in 2020, S. D. Fabiyi et al [14] combined hyperspectral image data and high-resolution RGB image data to segment rice seeds and more accurately acquire their spatial and spectral information to determine their categories, achieving a maximum recognition rate of 98.59% across six rice species.…”

Section: Introductionmentioning

confidence: 99%

SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure

Liu

Zeng

et al. 2023

Sensors

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During the rice quality testing process, the precise segmentation and extraction of grain pixels is a key technique for accurately determining the quality of each seed. Due to the similar physical characteristics, small particles and dense distributions of rice seeds, properly analysing rice is a difficult problem in the field of target segmentation. In this paper, a network called SY-net, which consists of a feature extractor module, a feature pyramid fusion module, a prediction head module and a prototype mask generation module, is proposed for rice seed instance segmentation. In the feature extraction module, a transformer backbone is used to improve the ability of the network to learn rice seed features; in the pyramid fusion module and the prediction head module, a six-layer feature fusion network and a parallel prediction head structure are employed to enhance the utilization of feature information; and in the prototype mask generation module, a large feature map is used to generate high-quality masks. Training and testing were performed on two public datasets and one private rice seed dataset. The results showed that SY-net achieved a mean average precision (mAP) of 90.71% for the private rice seed dataset and an average precision (AP) of 16.5% with small targets in COCO2017. The network improved the efficiency of rice seed segmentation and showed excellent application prospects in performing rice seed quality testing.

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Hyperspectral image segmentation: a comprehensive survey

Cited by 13 publications

References 97 publications

Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics

Adaptive Multi-class Segmentation Model of Aggregate Image Based on Improved Sparrow Search Algorithm

SY-Net: A Rice Seed Instance Segmentation Method Based on a Six-Layer Feature Fusion Network and a Parallel Prediction Head Structure

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