Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

Chen, Shih‐Yu; Lin, Chinsu; Tai, Chia-Hui; Chuang, Shang-Ju

doi:10.3390/rs10010096

Cited by 25 publications

(28 citation statements)

References 56 publications

(58 reference statements)

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“…Our proposed algorithm first identified the desired signature of insect damaged beans as the d (desired signature) in CEM for the detection of other similar beans. Optimal signature generation process (OSGP) [29,30] was used to find the optimal desired spectral signature. As the CEM needs only one desired spectral signature for detection, the quality of the detection result is very sensitive to the desired spectral signature.…”

Section: Optimal Signature Generation Processmentioning

confidence: 99%

“…The greatest challenge in the detection of insect damaged coffee beans is that the damaged areas provide very limited spatial information and are generally difficult to visualize from data. CEM, which is a hyperspectral target detection algorithm, is effective in dealing with the subpixel detection problem [24,25,29,30]. As mentioned in Section 2.3.1, CEM requires only one desired spectral signature for detection, thus, the quality of the detection result is very sensitive to the desired spectral signature.…”

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confidence: 99%

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Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

Chen

Chang

et al. 2020

Remote Sensing

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The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.

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Section: Optimal Signature Generation Processmentioning

confidence: 99%

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confidence: 99%

Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

Chen

Chang

et al. 2020

Remote Sensing

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“…In contrast, the use of spectral continuum features of the SWIR water absorption wavelengths can significantly improve diagnosing water stress in vegetation. Additional functional plant traits such as live/dead fraction of canopy materials [53], phenological events [50,54,55], and canopy structure and composition [52] that are remotely observable from space will cause different sorts of variations in spectral reflectance. This is particularly evident as a pixel is a mixture of tree and non-vegetation.…”

Section: Potential Benefits Of the Glgcm Technique For Diagnosing Formentioning

confidence: 99%

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

Lin

2018

Remote Sensing

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The continuum of the SWIR (short-wave infrared) signals from 1320 to 1650 nm contains valuable information for effectively diagnosing water, chlorophyll, and nitrogen content. The SWIR spectra of in situ spectroradiometric data and airborne spectrometric images are frequently contaminated by significant noise. Based on a Logistic-Gaussian complex signal model (LGCM), the noise-free signals at 1330-1349 and 1411-1430 nm wavelengths can provide critical bases for restoring the 1350-1410 nm wavelength signals for a single point of data. This paper proposes a generalized LGCM (GLGCM) technique to expand the ability of LGCM to process large data with variant reflectance values. A 12-year-old red cypress plantation located in a central Taiwan temperate forest was selected for this study. Hundreds of reflectance spectra of tree crowns were obtained using an ASD FR Spectroradiometer. The in-laboratory blank test showed that the GLGCM technique was able to achieve sufficient performance with an RMSE (root mean square error) of 0.0015 ± 0.0005 and 0.0011 ± 0.0005 for the front-edge and end-edge signal bases respectively, and 0.0014 ± 0.0006 in between the two signal bases. A significant level of noise between −0.2 and 0.4 was successfully removed from the in situ contaminated reflectance in the 1350-1410 nm wavelengths. The estimation bias for the signals of front-edge and end-edge bases was low, averaging 0.0031 ± 0.0003 and 0.0032 ± 0.0012. The consistency between the blank test and the in situ experimental results indicates that the GLGCM technique has potential in using batch processing to fix the problem of the noisy SWIR spectra in spectroradiometeric data and also airborne spectrometric images.

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“…However, target detection algorithms were sensitive to the desired target in the final results. After that, Chen et al [ 76 ] proposed the Optimal Signature Generation Process (OSGP) and adaptive window-based Constrained Energy Minimization (CEM) [ 77 ], which could provide steadier detection results and reduce the occurrence of false alarms. Another method is based on data preprocessing, in which the target of detection is enhanced by data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Chen

Lin

et al. 2021

Sensors

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The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.

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Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

Cited by 25 publications

References 56 publications

Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

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