Constrained-Target Band Selection for Multiple-Target Detection

Wang, Yulei; Wang, Lin; Yu, Chunyan; Zhao, En-Guang; Song, Ming; Wen, Chia-Hsien; Chang, Chein‐I

doi:10.1109/tgrs.2019.2904264

Cited by 40 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this regard, CEM has been used to design a new band selection method called constraint band selection (CBS) [19], and the resulting minimum variance from CBS is used to calculate the priority score to rank the frequency bands. Conceptually, constrained-target band selection (CTBS) [25,26] is slightly different from CBS, as CBS only focuses on the band of interest, while CTBS simultaneously takes advantage of the target signature and the band of interest. First, it specifies the signal d of a target, and then constrains d to minimize the variance caused by the background signal through the FIR filter.…”

Section: Constrained Energy Minimization (Cem)mentioning

confidence: 99%

“…In contrast to MinV-BP, the concept of Max V-BP [23,25] is to first remove b l from the band set Ω, and the variance is calculated as follows:…”

Section: Maximum Variance Band Prioritization (Maxv-bp)mentioning

confidence: 99%

“…In contrast to SF-CTBS using the MinV_BP criteria in (3), SB-CTBS [25] applies the MaxV_BP as the criterion by using the leave-one-out method to select the optimal bands. For each single band, b l , assumes band subset Ω − b l 1 , which removes b l from the full band.…”

Section: Sequential Backward-target Band Selection (Sb-ctbs)mentioning

confidence: 99%

See 2 more Smart Citations

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

Chen

Chang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: Constrained Energy Minimization (Cem)mentioning

confidence: 99%

“…In contrast to MinV-BP, the concept of Max V-BP [23,25] is to first remove b l from the band set Ω, and the variance is calculated as follows:…”

Section: Maximum Variance Band Prioritization (Maxv-bp)mentioning

confidence: 99%

See 1 more Smart Citation

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

Chen

Chang

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Based on the concept of CEM, Geng [27] proposed a sparse constrained band selection (SCBS), which is convenient for solving the global optimal solution and avoids the complicated subset search process. Wang et al [28] proposed a new multi-target detection BS method, MinV-BP, which minimized the variance generated by the target of interest to measure the priority of the band.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a real-time detection method for hyperspectral underwater targets based on BS. First of all, in order to solve the problems suffering from a large amount of redundant data and slow acquisition and processing speed of hyperspectral image data, a BS method is designed in combination with MinV-BP [28] and OIF [23] to select an optimal band subset with strong ability in characterizing specific targets, called constrained-target OIF band selection (CTOIFBS). Then, an underwater multi-spectral sensor composed of the selected bands is particularly designed to collect images to overcome the difficulty of long transmission time of the complete hyperspectral image.…”

Section: Introductionmentioning

confidence: 99%

Underwater Hyperspectral Target Detection with Band Selection

Shang

Sun

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS.

show abstract

Introduction

Chang

2022

Advances in Hyperspectral Image Processing Techniques

View full text Add to dashboard Cite

Constrained-Target Band Selection for Multiple-Target Detection

Cited by 40 publications

References 40 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

Underwater Hyperspectral Target Detection with Band Selection

Introduction

Contact Info

Product

Resources

About