Determination of optimal wavelet denoising parameters for red edge feature extraction from hyperspectral data

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

Remote Sensing ◽

10.3390/rs13081602 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1602

Author(s):

Qiaoqiao Sun ◽

Xuefeng Liu ◽

Salah Bourennane

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Spatial Information ◽

Hyperspectral Data ◽

Great Promise ◽

Learning Models ◽

Single Level ◽

Multiple Networks ◽

Multi Level ◽

Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

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Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽

10.1186/s13007-020-00693-3 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Shanjun Luo ◽

Yingbin He ◽

Qian Li ◽

Weihua Jiao ◽

Yaqiu Zhu ◽

...

Keyword(s):

Precision Agriculture ◽

Growth Stage ◽

Estimation Error ◽

Potato Yield ◽

Hyperspectral Data ◽

Single Stage ◽

Accurate Estimation ◽

Yield Estimation ◽

Area Index ◽

Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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Effect of wavelet denoising techniques on the determination of navel orange sugar content with near-infrared spectra

10.1117/12.685596 ◽

2006 ◽

Author(s):

Lijuan Xie ◽

Haibo Huang ◽

Yibin Ying ◽

Huishan Lu ◽

Xiaping Fu

Keyword(s):

Infrared Spectra ◽

Near Infrared ◽

Sugar Content ◽

Wavelet Denoising ◽

Navel Orange ◽

Near Infrared Spectra

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Fault Identification and Determination of Its Location in a HVDC System Based on Feature Extraction and Artificial Neural Network

Journal of The Institution of Engineers (India) Series B ◽

10.1007/s40031-021-00541-5 ◽

2021 ◽

Vol 102 (2) ◽

pp. 351-361

Author(s):

Nabamita Banerjee Roy

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Feature Extraction ◽

Fault Identification ◽

Hvdc System ◽

Artificial Neural

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Identification of Pose Gesture of Serving in Volleyball and three pointer shot in Basketball using Baysian classification& Image Edge Feature Extraction

IARJSET ◽

10.17148/iarjset.2021.8921 ◽

2021 ◽

Vol 8 (9) ◽

Author(s):

Dr. Ashish Shukla ◽

Mr. Vishwanath D Kale

Keyword(s):

Feature Extraction ◽

Classification Image ◽

Image Edge ◽

Edge Feature

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Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742184 ◽

2020 ◽

Vol 7 (4) ◽

pp. 745

Author(s):

Rizka Indah Armianti ◽

Achmad Fanany Onnilita Gaffar ◽

Arief Bramanto Wicaksono Putra

Keyword(s):

Feature Extraction ◽

Video Data ◽

Clustering Method ◽

Data Frame ◽

First Order ◽

Feature Extraction And Selection ◽

Different Shapes ◽

Frame Set ◽

Selection Of

Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap frame. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap frame-nya. Frame yang memiliki pola terbaik diantara frame lainnya disebut frame dominan. Penelitian ini bertujuan untuk menyeleksi frame dominan dari rangkaian frame dengan menerapkan metode K-means clustering untuk memperoleh centroid dominan (centroid dengan nilai tertinggi) yang digunakan sebagai dasar seleksi frame dominan. Dalam menyeleksi frame dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu Mean. Data hasil ekstraksi ciri berjumlah 93 data frame yang selanjutnya dikelompokkan menjadi 3 cluster menggunakan metode K-Means. Dari hasil clustering, centroid dominan terletak pada cluster 3 dengan nilai centroid 0.0177 dan terdiri dari 41 data frame. Selanjutnya diukur jarak kedekatan seluruh data cluster 3 terhadap centroid, data yang memiliki jarak terdekat dengan centroid itulah frame dominan. Hasil seleksi frame dominan ditunjukkan pada jarak antar centroid dengan anggota cluster, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai 0.0010 yang dimiliki oleh frame ke-59, ke-36 dan ke-35. AbstractThe object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.

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