scholarly journals New evaluation method to detect physiological stress in fruit trees by airborne hyperspectral image spectroscopy

2010 ◽  
Vol 16 (1) ◽  
Author(s):  
J. Tamás
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Physiological Stress ◽  
Incident Light ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Infrared Range ◽  
Image Spectroscopy

Nowadays airborne remote sensing data are increasingly used in precision agriculture. The fast space-time dependent localization of stresses in orchards, which allows for a more efficient application of horticultural technologies, could lead to improved sustainable precise management. The disadvantage of the near field multi and hyper spectroscopy is the spot sample taking, which can apply independently only for experimental survey in plantations. The traditional satellite images is optionally suitable for precision investigation because of the low spectral and ground resolution on field condition. The presented airborne hyperspectral image spectroscopy reduces above mentioned disadvantages and at the same time provides newer analyzing possibility to the user. In this paper we demonstrate the conditions of data base collection and some informative examination possibility. The estimating of the board band vegetation indices calculated from reflectance is well known in practice of the biomass stress examinations. In this method the N-dimension spectral data cube enables to calculate numerous special narrow band indexes and to evaluate maps. This paper aims at investigating the applied hyperspectral analysis for fruit tree stress detection. In our study, hyperspectral data were collected by an AISADUAL hyperspectral image spectroscopy system, with high (0,5-1,5 m) ground resolution. The research focused on determining of leaves condition in different fruit plantations in the peach orchard near Siófok. Moreover the spectral reflectance analyses could provide more information about plant condition due to changes in the absorption of incident light in the visible and near infrared range of the spectrum.

Near Infrared Hyperspectral Image Analysis Using R. Part 5: Animated Visualisation of Hyperspectral Data Using R and ImageJ

NIR news ◽  
2014 ◽  
Vol 25 (7) ◽  
pp. 15-17 ◽  
Author(s):  
Y. Dixit ◽  
R. Cama ◽  
C. Sullivan ◽  
L. Alvarez Jubete ◽  
A. Ktenioudaki
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Image Analysis

scholarly journals JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 2016 ◽  
Author(s):  
Tao Zhang ◽  
Puzhao Zhang ◽  
Weilin Zhong ◽  
Zhen Yang ◽  
Fan Yang
Keyword(s):  
Local Binary Pattern ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Spectral Characteristics ◽  
Three Dimensional ◽  
Real Data ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Deep Network

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

scholarly journals Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection

2021 ◽  
Vol 13 (18) ◽  
pp. 3649
Author(s):  
Giorgio Morales ◽  
John W. Sheppard ◽  
Riley D. Logan ◽  
Joseph A. Shaw
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Redundancy Analysis ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Computational Time ◽  
Redundant Data ◽  
Spectral Bands ◽  
Spectral Selection

Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.

scholarly journals Mineral mapping by hyperspectral remote sensing in West Greenland using airborne, ship-based and terrestrial platforms

2018 ◽  
pp. 47-50 ◽  
Author(s):  
Sara Salehi ◽  
Simon Mose Thaarup
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Geological Mapping ◽  
Infrared Range ◽  
Remote Measurement ◽  
Electromagnetic Spectrum ◽  
Similar Data ◽  
Data Set ◽  
Specific Chemical ◽  
West Greenland

While multispectral images have been in regular use since the 1970s, the widespread use of hyperspectral images is a relatively recent trend. This technology comprises remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. Regional geological mapping and mineral exploration are among the main applications that may benefit from hyperspectral technology. Minerals and rocks exhibit diagnostic spectral features throughout the electromagnetic spectrum that allow their chemical composition and relative abundance to be mapped. Most studies using hyperspectral data for geological applications have concerned areas with arid to semi-arid climates, and using airborne data collection. Other studies have investigated terrestrial outcrop sensing and integration with laser scanning 3D models in ranges of up to a few hundred metres, whereas less attention has been paid to ground-based imaging of more distant targets such as mountain ridges, cliffs or the walls of large pits. Here we investigate the potential of using such data in well-exposed Arctic regions with steep topography as part of regional geological mapping field campaigns, and to test how airborne hyperspectral data can be combined with similar data collected on the ground or from moving platforms such as a small ship. The region between the fjords Ikertoq and Kangerlussuaq (Søndre Strømfjord) in West Greenland was selected for a field study in the summer of 2016. This region is located in the southern part of the Palaeoproterozoic Nagssugtoqidian orogen and consists of high-grade metamorphic ortho- and paragneisses and metabasic rocks (see below). A regional airborne hyperspectral data set (i.e. HyMAP) was acquired here in 2002 (Tukiainen & Thorning 2005), comprising 54 flight lines covering an area of c. 7500 km2; 19 of these flight lines were selected for the present study (Fig. 1). The target areas visited in the field were selected on the basis of preliminary interpretations of HyMap scenes and geology (Korstgård 1979). Two different sensors were utilised to acquire the new hyperspectral data, predominantly a Specim AisaFenix hyperspectral scanner due to its wide spectral range covering the visible to near infrared and shortwave infrared parts of the electromagnetic spectrum. A Rikola Hyperspectral Imager constituted a secondary imaging system. It is much smaller and lighter than the Fenix scanner, but is spectrally limited to the visible near infrared range. The results obtained from combining the airborne hyperspectral data and the Rikola instrument are presented in Salehi (2018), this volume. In addition, representative samples of the main rock types were collected for subsequent laboratory analysis. A parallel study was integrated with geological and 3D photogrammetric mapping in Karrat region farther north in West Greenland (Rosa et al. 2017; Fig. 1).

scholarly journals Target detection algorithm for airborne thermal hyperspectral data

2014 ◽  
Vol XL-8 ◽  
pp. 827-832 ◽  
Author(s):  
R. Marwaha ◽  
A. Kumar ◽  
P. L. N. Raju ◽  
Y. V. N. Krishna Murthy
Keyword(s):  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Hyperspectral Data ◽  
High Spectral Resolution ◽  
Infrared Range ◽  
Digital Photograph ◽  
Minimum Noise Fraction ◽  
Noise Fraction ◽  
Minimum Noise

Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm<sup>&minus;1</sup> to 1280 cm<sup>&minus;1</sup> region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm<sup>&minus;1</sup> (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

scholarly journals Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

2016 ◽  
Vol 09 (06) ◽  
pp. 1650002 ◽  
Author(s):  
Manfei Xu ◽  
Luwei Zhou ◽  
Qiao Zhang ◽  
Zhisheng Wu ◽  
Xinyuan Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Information ◽  
Nir Spectroscopy ◽  
Pharmaceutical Formulations ◽  
Chemical Imaging ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Chemical Information ◽  
Chlorpheniramine Maleate ◽  
Homogeneity Assessment

Near infrared chemical imaging (NIR-CI) combines conventional near infrared (NIR) spectroscopy with chemical imaging, thus provides spectral and spatial information simultaneously. It could be utilized to visualize the spatial distribution of the ingredients in a sample. The data acquired using NIR-CI instrument are hyperspectral data cube (hypercube) containing thousands of spectra. Chemometric methodologies are necessary to transform spectral information into chemical information. Partial least squares (PLS) method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations. A series of samples which consisted of different CPM concentrations (w/w) were compressed and hypercube data were measured. The spectra extracted from the hypercube were used to establish the PLS model of CPM. The results of the model were [Formula: see text] 0.981, RMSEC 0.384%, RMSECV 0.483%, RMSEP 0.631%, indicating that this model was reliable.

scholarly journals Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

2020 ◽  
Vol 12 (6) ◽  
pp. 926 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James Shepherd
Keyword(s):  
New Zealand ◽  
Spectral Range ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Absolute Error ◽  
Hyperspectral Data ◽  
Aerial Images ◽  
Large Area ◽  
Monitoring Method ◽  
Stress Symptoms

The endemic New Zealand kauri trees (Agathis australis) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor.

scholarly journals The High-Performance Airborne Imaging Spectrometer HyPlant—From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products: Introduction of an Automatized Processing Chain

2019 ◽  
Vol 11 (23) ◽  
pp. 2760 ◽  
Author(s):  
Bastian Siegmann ◽  
Luis Alonso ◽  
Marco Celesti ◽  
Sergio Cogliati ◽  
Roberto Colombo ◽  
...  
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Image Data ◽  
Sampling Interval ◽  
Hyperspectral Data ◽  
Infrared Range ◽  
Canopy Reflectance ◽  
Imaging Spectrometer ◽  
Processing Chain ◽  
Technical Specifications

The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670–780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps.

scholarly journals Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera

2018 ◽  
Vol 58 (8) ◽  
pp. 1488 ◽  
Author(s):  
S. Rahman ◽  
P. Quin ◽  
T. Walsh ◽  
T. Vidal-Calleja ◽  
M. J. McPhee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Subcutaneous Fat ◽  
Ground Truth ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Total Fat ◽  
Hyperspectral Camera

The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.

scholarly journals Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification

2018 ◽  
Vol 7 (9) ◽  
pp. 349 ◽  
Author(s):  
Hongmin Gao ◽  
Yao Yang ◽  
Chenming Li ◽  
Hui Zhou ◽  
Xiaoyu Qu
Keyword(s):  
Precision Agriculture ◽  
Strong Correlation ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Composite Layer ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Model Parameters ◽  
High Level

A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications. It is also widely used in precision agriculture, marine monitoring, military reconnaissance and many other fields. In recent years, a convolutional neural network (CNN) has been successfully used in HSI classification and has provided it with outstanding capacity for improving classification effects. To get rid of the bondage of strong correlation among bands for HSI classification, an effective CNN architecture is proposed for HSI classification in this work. The proposed CNN architecture has several distinct advantages. First, each 1D spectral vector that corresponds to a pixel in an HSI is transformed into a 2D spectral feature matrix, thereby emphasizing the difference among samples. In addition, this architecture can not only weaken the influence of strong correlation among bands on classification, but can also fully utilize the spectral information of hyperspectral data. Furthermore, a 1 × 1 convolutional layer is adopted to better deal with HSI information. All the convolutional layers in the proposed CNN architecture are composed of small convolutional kernels. Moreover, cascaded composite layers of the architecture consist of 1 × 1 and 3 × 3 convolutional layers. The inputs and outputs of each composite layer are stitched as the inputs of the next composite layer, thereby accomplishing feature reuse. This special module with joint alternate small convolution and feature reuse can extract high-level features from hyperspectral data meticulously and comprehensively solve the overfitting problem to an extent, in order to obtain a considerable classification effect. Finally, global average pooling is used to replace the traditional fully connected layer to reduce the model parameters and extract high-dimensional features from the hyperspectral data at the end of the architecture. Experimental results on three benchmark HSI datasets show the high classification accuracy and effectiveness of the proposed method.

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