• 대한전기학회논문지:시스템및제어부문D
• /
• 제52권11호
• /
• pp.634-643
• /
• 2003

This paper presents a method for detecting skin tumors on chicken carcasses using hyperspectral images. It utilizes both fluorescence and reflectance image information in hyperspectral images. A detection system that is built on this concept can increase detection rate and reduce processing time, because the procedure for detection can be simplified. Chicken carcasses are examined first using band ratio FCM information of fluorescence image and it results in candidate regions for skin tumor. Next classifier selects the real tumor spots using PCA components information of reflectance image from the candidate regions. For the real world application, real-time processing is a key issue in implementation and the proposed method can accommodate the requirement by using a limited number of features to maintain the low computational complexity. Nevertheless, it shows favorable results and, in addition, uncovers meaningful spectral bands for detecting tumors using hyperspectral image. The method and findings can be employed in implementing customized chicken tumor detection systems.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.334-336
• /
• 2003

Satellite and airborne hyperspectral sensor images are suitable for investigating the vegetation state in agricultural land. However, image data obtained by an optical sensor inevitably includes mixels caused by high altitude observation. Therefore, mixel analysis method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. In this paper, we apply our agricultural independent component analysis (ICA) model to an airborne hyperspectral sensor image, which includes noise and fluctuation of coverage, and estimate pure spectra and the mixture ratio of crop and soil in agricultural land simultaneously.

• 한국멀티미디어학회논문지
• /
• 제16권1호
• /
• pp.19-28
• /
• 2013

In this research, we presented an effective clustering method based on ICA for the analysis of huge Raman hyperspectral dental data. The hyperspectral dataset captured by HR800 micro Raman spectrometer at UMKC-CRISP(University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties), has 569 local points. Each point has 1,005 hyperspectal dentin data. We compared the clustering effectiveness and the clustering time for the case of using all dataset directly and the cases of using the scores after PCA and ICA. As the result of experiment, the cases of using the scores after PCA and ICA showed, not only more detailed internal dentin information in the aspect of medical analysis, but also about 7~19 times much shorter processing times for clustering. ICA based approach also presented better performance than that of PCA, in terms of the detailed internal information of dentin and the clustering time. Therefore, we could confirm the effectiveness of ICA for the analysis of Raman hyperspectral dental data.

• 지적과 국토정보
• /
• 제46권2호
• /
• pp.239-251
• /
• 2016

밴드 수가 많고 밴드 폭이 좁은 초분광영상은 기존의 다중 분광 영상에 비해 각 픽셀이 함유하고 있는 정보가 많아 영상을 이용한 토지피복분류를 하는데 있어 최적의 영상으로 평가 받고 있다. 하지만 초분광영상의 높은 분광해상도로 부터 증가된 데이터의 용량과 노이즈로 인해 다중분광영상을 분석하는 기법을 그대로 적용하기에는 효용성이 떨어진다. 초분광영상의 분석 기법으로서 벡터의 내적을 활용하는 SAM(Spectral Angle Mapping)은 연속적인 스펙트럼을 보이는 초분광영상의 특성을 해석하는데 가장 보편적인 방법이다. 이에 본 연구에서는 분광라이브러리를 이용한 초분광영상의 토지피복분류를 수행하기 위해 SAM기법을 채택하였으나 대기영향의 노이즈로 인해 낮은 정확도를 보였다. 이를 보안하기 위한 방법으로서 Decision Tree 기법을 제안하였고 그 결과, 분류 정확도를 향상시킬 수 있었다.

• 한국근적외분광분석학회:학술대회논문집
• /
• 한국근적외분광분석학회 2001년도 NIR-2001
• /
• pp.3106-3106
• /
• 2001

Imaging spectrometry or hyperspectral imaging is a recent development that makes possible quantitative and qualitative measurement for food quality and safety. This paper presents the research results that a hyperspectral imaging system can be used effectively for detecting fecal (from duodenum, cecum, and colon) and ingesta contamination on poultry carcasses from the different feed meals (wheat, mile, and corn with soybean) for poultry safety inspection. A hyperspectral imaging system has been developed and tested for the identification of fecal and ingesta surface contamination on poultry carcasses. Hypercube image data including both spectral and spatial domains between 430 and 900 nm were acquired from poultry carcasses with fecal and ingesta contamination. A transportable hyperspectral imaging system including fiber optically fabricated line lights, motorized lens control for line scans, and hypercube image data from contaminated carcasses with different feeds are presented. Calibration method of a hyperspectral imaging system is demonstrated using different lighting sources and reflectance panels. Principal Component and Minimum Noise Fraction transformations will be discussed to characterize hyperspectral images and further image processing algorithms such as image band ratio of dual-wavelength images and its histogram stretching with thresholding process will be demonstrated to identify fecal and ingesta materials on poultry carcasses. This algorithm could be further applied for real-time classification of fecal and ingesta contamination on poultry carcasses in the poultry processing line.

• 보존과학회지
• /
• 제31권4호
• /
• pp.395-402
• /
• 2015

이 연구에서는 하이퍼스펙트럴 이미지를 활용하여 석조문화재의 훼손상태 모니터링에 대한 활용성을 검토하였다. 이를 위해 하이퍼스펙트럴 데이터의 보정방법, 영상분류 및 정규화 식생지수 산출방법을 석조문화재에 적용하였다. 이 결과 각 물질의 분광정보를 기반으로 한 객관적인 훼손지도 작성, 정밀도 높은 훼손율의 산출 및 식생들의 활력도 모델작성 등 다양한 분석이 가능하였다. 따라서 하이퍼스펙트럴 이미지 분석을 활용하여 석조문화재를 모니터링 한다면 효율적으로 훼손상태 변화를 파악할 수 있을 것이다.

• 대한공간정보학회지
• /
• 제22권3호
• /
• pp.155-160
• /
• 2014

본 논문에서는 각기 다른 3가지 해상도로 촬영된 항공 초분광영상을 이용하여 건물, 도로, 산림 등 8가지 분류군에 대해 토지피복분류를 실시하고 정확도를 비교하는 연구를 수행하였다. 연구는 24밴드(0.5m 공간해상도), 48밴드(1.0m 공간해상도), 96밴드(1.5m 공간해상도)로 각각 1000m, 2000m, 3000m고도에서 촬영된 초분광영상을 이용하여 8가지 클래스에 대해 토지피복분류를 수행하였다. 그 결과 2000m고도에서 촬영된 48밴드 초분광영상을 이용하여 분류한 영상이 가장 높은 분류정확도를 보였고, 24밴드, 96밴드 순으로 분류정확도가 높게 나타났다. 초분광영상 활용에 있어서 1m 공간해상도에 48개밴드를 사용하여 토지피복분류를 수행함에 있어 적합함을 확인하였고 항공 초분광영상을 활용한 주제도 제작과 관련하여 정확도와 실용성 면에서 공간정보 품질이 개선될 것으로 기대한다.

• 농업과학연구
• /
• 제47권3호
• /
• pp.645-655
• /
• 2020

Bacteria are a very common cause of food poisoning. Moreover, bacteria form biofilms to protect themselves from harsh environments. Conventional detection methods for foodborne bacterial pathogens including the plate count method, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction (PCR) assays require a lot of time and effort. Hyperspectral imaging has been used for food safety because of its non-destructive and real-time detection capability. This study assessed the feasibility of using hyperspectral imaging and machine learning techniques to detect biofilms formed by Escherichia coli. E. coli was cultured on a high-density polyethylene (HDPE) coupon, which is a main material of food processing facilities. Hyperspectral fluorescence images were acquired from 420 to 730 nm and analyzed by a single wavelength method and machine learning techniques to determine whether an E. coli culture was present. The prediction accuracy of a biofilm by the single wavelength method was 84.69%. The prediction accuracy by the machine learning techniques were 87.49, 91.16, 86.61, and 86.80% for decision tree (DT), k-nearest neighbor (k-NN), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA), respectively. This result shows the possibility of using machine learning techniques, especially the k-NN model, to effectively detect bacterial pathogens and confirm food poisoning through hyperspectral images.

• Ecology and Resilient Infrastructure
• /
• 제6권3호
• /
• pp.171-177
• /
• 2019

초분광 영상은 기존 다중분광 영상에 비해 보다 세밀한 분석이 가능하며 감지가 어려운 지표 성질의 분석에 유용하게 활용될 수 있다. 따라서 본 연구에서는 수심에 대한 실측데이터와 드론 기반의 영상을 이용하여 하천환경 정보를 획득하는 것이 목적으로써 이를 위해 드론 기반의 초분광 센서를 활용하여 1개 측선 100개 지점에 대한 영상값을 취득하였으며 ADCP를 통해 확보된 실제 수심정보와 비교하여 상관관계를 분석하였다. ADCP 측정결과 중앙으로 갈수록 수심이 깊어지는 경향을 보이고 있으며 수심은 평균 0.81 m로 나타났다. 초분광 영상 분석 결과 최대 강도가 가장 높은 지점은 645, 가장 낮은 지점은 278이며 실제 수심과 초분광 영상분석결과의 상관성을 분석한 결과 최대 강도값이 감소할수록 수심은 증가하는 것으로 나타났다.

• Current Optics and Photonics
• /
• 제5권3호
• /
• pp.270-277
• /
• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.