Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

1995 ◽  
Vol 9 (3) ◽  
pp. 477-483 ◽  
Author(s):  
Hubert W. Carson ◽  
Lawrence W. Lass ◽  
Robert H. Callihan
Keyword(s):  
Supervised Classification ◽  
Digital Images ◽  
Ground Cover ◽  
Unsupervised Classification ◽  
Digital Imagery ◽  
Charge Coupled Device ◽  
Spectral Filters ◽  
A Charge ◽  
Northern Idaho

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

scholarly journals Denoising of digital images through PSO based pixel classification

2013 ◽  
Vol 3 (4) ◽  
Author(s):  
Somnath Mukhopadhyay ◽  
Jyotsna Mandal
Keyword(s):  
Digital Images ◽  
Fitness Function ◽  
Unsupervised Classification ◽  
Noisy Image ◽  
Pixel Classification ◽  
Visual Effects ◽  
Swarm Optimization ◽  
Image Pixels ◽  
Noise Models

AbstractThis paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

scholarly journals The Inspection of Raw- Silk Defects Using Image Vision

2018 ◽  
Vol 13 (3) ◽  
pp. 155892501801300 ◽  
Author(s):  
Qingtian Pan ◽  
Miao Chen ◽  
Baoqi Zuo ◽  
Yucai Hu
Keyword(s):  
Detection System ◽  
Image Sensor ◽  
Support Vector ◽  
Technological Parameters ◽  
Image Capture ◽  
Charge Coupled Device ◽  
The Moment ◽  
A Charge ◽  
Ccd Image

The inspection of defects is one of the most important aspects in the quality inspection of raw silk. We introduce a raw-silk defect detection system based on image vision and image analysis that is accurate and objective. In the experimental phase, we develop an image-acquisition section—which includes a charge-coupled device (CCD) image sensor, a telecentric lens, a light source, and a raw-silk winding device to capture the raw silk images steadily. After the image capture stage, an image-processing section tasked with threshold segmentation and morphology operations is carried out to obtain the defects of raw silk. To classify the raw-silk defects accurately and quickly, we propose an area method for the classification of raw-silk defects into five categories: larger defects, large defects, common defects, small defects, and smaller defects. Meanwhile, in order to recognize the common raw-silk defects—e.g., Bavella silk, nodes, and loose ends—that cannot be detected by the Uster evenness tester, the moment invariants of each segmented region of the images are extracted and used as the input of support vector machine(SVM).A SVM is designed as a classifier to recognize the samples. The experimental results show that the proposed method can recognize these common raw-silk defects effectively. According to the new classification and accurate recognition of raw-silk defects using the proposed method, we can improve the inspection standards for raw silk and advise raw-silk reeling enterprises seeking to optimize the technological parameters.

Detection of Yellow Starthistle (Centaurea solstitialis) and Common St. Johnswort (Hypericum perforatum) with Multispectral Digital Imagery

1996 ◽  
Vol 10 (3) ◽  
pp. 466-474 ◽  
Author(s):  
Lawrence W. Lass ◽  
Hubert W. Carson ◽  
Robert H. Callihan
Keyword(s):  
Hypericum Perforatum ◽  
Ground Cover ◽  
Centaurea Solstitialis ◽  
Plant Community Composition ◽  
Digital Imagery ◽  
Population Changes ◽  
Yellow Starthistle ◽  
Spectral Filters ◽  
Species Population ◽  
Semiarid Rangeland

Ground-based surveys to regularly document the size, shape, and location of weed populations usually are not economically feasible. Digital images of vegetation in semiarid rangeland were obtained from four charge-coupled devices with spectral filters mounted in an airplane. The ability to distinguish yellow starthistle and common St. Johnswort from other rangeland vegetation in images with 0.5, 1, 2, and 4 m spatial resolution was assessed. Detection, positioning, and measurements of size and density of yellow starthistle and common St. Johnswort colonies with densities as low as 30% ground cover were possible at all those resolutions. Images indicated yellow starthistle occupied about one-third of a 180-ha study area. Images taken in June indicated 2 to 10 ha less yellow starthistle ranging from 30 to 100% cover than images taken in July for all resolutions. Images indicated common St. Johnswort occupied less than 4 ha of the 180-ha study area. This procedure provides a method to establish baseline plant community composition and a way to monitor species population changes and dispersal over time.

Photometric Application of the Gram Stain Method To Characterize Natural Bacterial Populations in Aquatic Environments

1998 ◽  
Vol 64 (2) ◽  
pp. 742-747 ◽  
Author(s):  
H. Saida ◽  
N. Ytow ◽  
H. Seki
Keyword(s):  
Wall Structure ◽  
Image Analyzer ◽  
Gram Stain ◽  
Bacterial Populations ◽  
Charge Coupled Device ◽  
Gram Positive Bacteria ◽  
A Charge ◽  
Dichotomous Classification

ABSTRACT The Gram stain method was applied to the photometric characterization of aquatic bacterial populations with a charge-coupled device camera and an image analyzer. Escherichia coli andBacillus subtilis were used as standards of typical gram-negative and gram-positive bacteria, respectively. A mounting agent to obtain clear images of Gram-stained bacteria on Nuclepore membrane filters was developed. The bacterial stainability by the Gram stain was indicated by the Gram stain index (GSI), which was applicable not only to the dichotomous classification of bacteria but also to the characterization of cell wall structure. The GSI spectra of natural bacterial populations in water with various levels of eutrophication showed a distinct profile, suggesting possible staining specificity that indicates the presence of a particular bacterial population in the aquatic environment.

scholarly journals Semi-supervised time series classification method for quantum computing

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sheir Yarkoni ◽  
Andrii Kleshchonok ◽  
Yury Dzerin ◽  
Florian Neukart ◽  
Marc Hilbert
Keyword(s):  
Time Series ◽  
Quantum Computing ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Set Cover ◽  
Time Series Classification ◽  
Reconstruction Problem ◽  
Training Set ◽  
Set Cover Problem

AbstractIn this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.

Classification

2014 ◽  
Author(s):  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
Alexander Gray ◽  
Andrew J. Connolly ◽  
Jacob T. VanderPlas ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Nearest Neighbor ◽  
Probability Distributions ◽  
Joint Probability ◽  
Unsupervised Classification ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Visual Appearance ◽  
Joint Probability Distributions

Chapter 6 described techniques for estimating joint probability distributions from multivariate data sets and for identifying the inherent clustering within the properties of sources. This approach can be viewed as the unsupervised classification of data. If, however, we have labels for some of these data points (e.g., an object is tall, short, red, or blue) we can utilize this information to develop a relationship between the label and the properties of a source. We refer to this as supervised classification, which is the focus of this chapter. The motivation for supervised classification comes from the long history of classification in astronomy. Possibly the most well known of these classification schemes is that defined by Edwin Hubble for the morphological classification of galaxies based on their visual appearance. This chapter discusses generative classification, k-nearest-neighbor classifier, discriminative classification, support vector machines, decision trees, and evaluating classifiers.

Clustering Analysis and Algorithms

2011 ◽  
pp. 389-396
Author(s):  
Xiangji Huang
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Clustering Analysis ◽  
Supervised Classification ◽  
Unsupervised Classification ◽  
Automatic Classification ◽  
Multidimensional Space ◽  
The Difference

Clustering is the process of grouping a collection of objects (usually represented as points in a multidimensional space) into classes of similar objects. Cluster analysis is a very important tool in data analysis. It is a set of methodologies for automatic classification of a collection of patterns into clusters based on similarity. Intuitively, patterns within the same cluster are more similar to each other than patterns belonging to a different cluster. It is important to understand the difference between clustering (unsupervised classification) and supervised classification.

Sign in / Sign up

Export Citation Format

Share Document