scholarly journals A Tool for Analysis of Spectral Indices for Remote Sensing of Vegetation and Crops Using Hyperspectral Images

2019 ◽  
pp. 51-58
Author(s):  
David Ruiz Hidalgo ◽  
Bladimir Bacca Cortés ◽  
Eduardo Caicedo Bravo
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Infrared Imaging ◽  
Software Tool ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Spectral Indices ◽  
Imaging Spectrometer ◽  
Software Application ◽  
Erdas Imagine

Food requirements in the world have increased, evidencing the necessity to improve standard techniques of agricultural production. To do so, one option is through technological elements like hyperspectral remote sensing of vegetation and crops. Remote sensing and hyperspectral imagery are not invasive methods. They allow covering large land space in a reduced amount of time. These features have done the hyper-spectral remote sensing a powerful tool used in precision agriculture. This paper presents a software application to process hyperspectral images and generating pseudo-color images computed using spectral indices. This work uses the hyperspectral images were taken by Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor, which was designed by the NASA. The software application aims to show different elements associated with the hyperspectral remote sensing of vegetation and crops. Functional tests are presented to verify the software requirements. Finally, quantitative results are reported comparing the results of the software proposes in this work with the ERDAS Imagine software tool.

PROGRESS IN DEVELOPING A HYPERSPECTRAL REMOTE SENSING LIBRARY AND SOFTWARE TOOL FOR FINDING PERSONS IN THE ENVIRONMENT

2017 ◽  
Author(s):  
Mark P.S. Krekeler ◽  
◽  
Michelle Burke ◽  
C. Scott Allen ◽  
Barrett Sather ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Software Tool ◽  
Hyperspectral Remote Sensing

Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends

2020 ◽  
pp. 121-146 ◽  
Author(s):  
Prachi Singh ◽  
Prem Chandra Pandey ◽  
George P. Petropoulos ◽  
Andrew Pavlides ◽  
Prashant K. Srivastava ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Present Status ◽  
Hyperspectral Remote Sensing ◽  
Future Trends

An evaluation of short‐wave‐infrared (SWIR) data from the AVIRIS and GEOSCAN instruments for mineralogical mapping at Cuprite, Nevada

Geophysics ◽  
1991 ◽  
Vol 56 (9) ◽  
pp. 1432-1440 ◽  
Author(s):  
Simon J. Hook ◽  
Christopher D. Elvidge ◽  
Michael Rast ◽  
Hiroshi Watanabe
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Short Wave ◽  
Spectral Features ◽  
Imaging Spectrometer ◽  
Sensing Systems ◽  
Alteration Minerals ◽  
Remote Sensing Systems ◽  
Residual Algorithm ◽  
Color Composite

An evaluation was performed on SWIR (2000–2400 nm) data from two airborne remote sensing systems for discriminating and identifying alteration minerals at Cuprite, Nevada. The data were acquired by the NASA Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and the GEOSCAN Mk II multispectral scanner. The evaluation involved comparison of processed imagery and image‐derived spectra with existing alteration maps and laboratory spectra of rock samples from Cuprite. Results indicate that both the AVIRIS and GEOSCAN data permit the discrimination of areas of alunite, buddingtonite, kaolinite, and silicification using color composite images formed from three SWIR bands processed with either the decorrelation stretch or a log residual algorithm. The laboratory spectral features of alunite, kaolinite and buddingtonite could be seen clearly only in the log residual processed AVIRIS data. However, this does not preclude their identification with the GEOSCAN data.

scholarly journals Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools

2021 ◽  
Vol 13 (5) ◽  
pp. 1020
Author(s):  
Véronique Achard ◽  
Pierre-Yves Foucher ◽  
Dominique Dubucq
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Infrared Imaging ◽  
Oil Spills ◽  
Oil Pollution ◽  
Spectral Unmixing ◽  
Controlled Experiment ◽  
Spectral Indices ◽  
Imaging Spectrometer ◽  
First Case

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution.

scholarly journals An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Youkyung Han ◽  
Anjin Chang ◽  
Seokkeun Choi ◽  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

scholarly journals Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3587 ◽  
Author(s):  
Chenming Li ◽  
Simon X. Yang ◽  
Yao Yang ◽  
Hongmin Gao ◽  
Jia Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Infrared Imaging ◽  
Remote Sensing Image ◽  
Image Features ◽  
Step Size ◽  
Imaging Spectrometer ◽  
Remote Sensing Image Classification

In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.

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