Contaminant detection on poultry carcasses using hyperspectral data: Part I. Algorithms for selection of individual wavebands

2007 ◽  
Author(s):  
Songyot Nakariyakul ◽  
David P. Casasent
Keyword(s):  
Hyperspectral Data ◽  
Contaminant Detection ◽  
Selection Of

A Study on Selecting Segmentation Object of Object-Oriented Inversion Based on Hyperspectral Chlorophyll-A in Chaohu Lake

2014 ◽  
Vol 675-677 ◽  
pp. 1153-1157
Author(s):  
Xue Jiao Hou ◽  
Jing Liu ◽  
Wei Cui
Keyword(s):  
Chlorophyll A ◽  
Object Oriented ◽  
Hyperspectral Data ◽  
Single Band ◽  
Quality Data ◽  
Chaohu Lake ◽  
Band Model ◽  
Water Quality Data ◽  
Combination Model ◽  
Selection Of

Beased on in Situ Water Quality Data and Hyperspectral Data from HJ-1A satellite in Chaohu Lake, through Contrasting the Object-Oriented Chlorophyll-a Inversion Precision Of single Band with Two-Band Model, the Results Show: (1) In Hyperspectral Object-Oriented Remote Sensing inversion, the Inversion Effect of Choosing Combination Model to Segment Is superior to that of Choosing the Single Band Directly, and Using Combination model to Segment can Certain Degree Solve the Problem that Commercial Softwares cannot Segment all Hyperspectral Data at the same Time.(2)When Inversing Chlorophyll-a Concentration with Hyperspectral Data, the Single Bands Constituting the Optimal Model Are not Always in the Traditional Characteristics Band Range of Chlorophyll-a. so All bands should be Comprehensively Analyzed to take Full Advantages Of hyperspectral Data when Inversing. these Conclusions Will provide Basis for the Future Segmentation Object Selection of Object-Orientedon Hyperspectral Lakes Chlorophyll-a Inversion and Certain Reference for Band Selection of Hyperspectral Inversion Model.

scholarly journals Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1020 ◽  
Author(s):  
Bollandsås ◽  
Ørka ◽  
Dalponte ◽  
Gobakken ◽  
Næsset
Keyword(s):  
Atmospheric Correction ◽  
Laser Scanner ◽  
Field Measurements ◽  
Site Index ◽  
Hyperspectral Data ◽  
Average Height ◽  
Single Time ◽  
Development Site ◽  
Corrected Data ◽  
Selection Of

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

scholarly journals Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

2020 ◽  
Vol 12 (12) ◽  
pp. 1983
Author(s):  
Kevin Chow ◽  
Dion Eustathios Olivier Tzamarias ◽  
Miguel Hernández-Cabronero ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Compression Ratio ◽  
Hyperspectral Data ◽  
Variable Length ◽  
Direct Access ◽  
Access Time ◽  
Competitive Performance ◽  
K Value ◽  
Data Elements ◽  
Selection Of

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.

scholarly journals Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

2019 ◽  
Vol 11 (11) ◽  
pp. 1298 ◽  
Author(s):  
Ahmed Laamrani ◽  
Aaron A. Berg ◽  
Paul Voroney ◽  
Hannes Feilhauer ◽  
Line Blackburn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Southern Ontario ◽  
Spectral Bands ◽  
Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

scholarly journals Detection of New Zealand Kauri Trees with AISA Aerial Hyperspectral Data for Use in Multispectral Monitoring

2019 ◽  
Vol 11 (23) ◽  
pp. 2865 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James Shepherd ◽  
David A. Norton
Keyword(s):  
New Zealand ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Optical Remote Sensing ◽  
Pixel Resolution ◽  
Water Index ◽  
Main Challenge ◽  
Representative Selection ◽  
Selection Of

The endemic New Zealand kauri trees (Agathis australis) are of major importance for the forests in the northern part of New Zealand. The mapping of kauri locations is required for the monitoring of the deadly kauri dieback disease (Phytophthora agathidicida (PTA)). In this study, we developed a method to identify kauri trees by optical remote sensing that can be applied in an area-wide campaign. Dead and dying trees were separated in one class and the remaining trees with no to medium stress symptoms were defined in the two classes “kauri” and “other”. The reference dataset covers a representative selection of 3165 precisely located crowns of kauri and 21 other canopy species in the Waitakere Ranges west of Auckland. The analysis is based on an airborne hyperspectral AISA Fenix image (437–2337 nm, 1 m2 pixel resolution). The kauri spectra show characteristically steep reflectance and absorption features in the near-infrared (NIR) region with a distinct long descent at 1215 nm, which can be parameterised with a modified Normalised Water Index (mNDWI-Hyp). With a Jeffries–Matusita separability over 1.9, the kauri spectra can be well separated from 21 other canopy vegetation spectra. The Random Forest classifier performed slightly better than Support Vector Machine. A combination of the mNDWI-Hyp index with four additional spectral indices with three red to NIR bands resulted in an overall pixel-based accuracy (OA) of 91.7% for crowns larger 3 m diameter. While the user’s and producer’s accuracies for the class “kauri” with 94.6% and 94.8% are suitable for management purposes, the separation of “dead/dying trees” from “other” canopy vegetation poses the main challenge. The OA can be improved to 93.8% by combining “kauri” and “dead/dying” trees in one class, separate classifications for low and high forest stands and a binning to 10 nm bandwidths. Additional wavelengths and their respective indices only improved the OA up to 0.6%. The method developed in this study allows an accurate location of kauri trees for an area-wide mapping with a five-band multispectral sensor in a representative selection of forest ecosystems.

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