scholarly journals Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping

2021 ◽  
Vol 13 (13) ◽  
pp. 2520
Author(s):  
Dongdong Ma ◽  
Tanzeel U. Rehman ◽  
Libo Zhang ◽  
Hideki Maki ◽  
Mitchell R. Tuinstra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Environmental Conditions ◽  
Modeling Method ◽  
Hyperspectral Images ◽  
Solar Irradiation ◽  
Plant Phenotyping ◽  
Sensing Applications ◽  
Aerial Remote Sensing ◽  
Data Variation

Aerial imaging technologies have been widely applied in agricultural plant remote sensing. However, an as yet unexplored challenge with field imaging is that the environmental conditions, such as sun angle, cloud coverage, temperature, and so on, can significantly alter plant appearance and thus affect the imaging sensor’s accuracy toward extracting plant feature measurements. These image alterations result from the complicated interaction between the real-time environments and plants. Analysis of these impacts requires continuous monitoring of the changes through various environmental conditions, which has been difficult with current aerial remote sensing systems. This paper aimed to propose a modeling method to comprehensively understand and model the environmental influences on hyperspectral imaging data. In 2019, a fixed hyperspectral imaging gantry was constructed in Purdue University’s research farm, and over 8000 repetitive images of the same corn field were taken with a 2.5 min interval for 31 days. Time-tagged local environment data, including solar zenith angle, solar irradiation, temperature, wind speed, and so on, were also recorded during the imaging time. The images were processed for phenotyping data, and the time series decomposition method was applied to extract the phenotyping data variation caused by the changing environments. An artificial neural network (ANN) was then built to model the relationship between the phenotyping data variation and environmental changes. The ANN model was able to accurately predict the environmental effects in remote sensing results, and thus could be used to effectively eliminate the environment-induced variation in the phenotyping features. The test of the normalized difference vegetation index (NDVI) calculated from the hyperspectral images showed that variance in NDVI was reduced by 79%. A similar performance was confirmed with the relative water content (RWC) predictions. Therefore, this modeling method shows great potential for application in aerial remote sensing applications in agriculture, to significantly improve the imaging quality by effectively eliminating the effects from the changing environmental conditions.

scholarly journals Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2407
Author(s):  
Hojun You ◽  
Dongsu Kim
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Optical Flow ◽  
Hyperspectral Imaging ◽  
Spatial Resolution ◽  
Template Matching ◽  
Hyperspectral Images ◽  
Spatial Accuracy ◽  
Flow Algorithm ◽  
Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

scholarly journals A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 263
Author(s):  
Amal Altamimi ◽  
Belgacem Ben Ben Youssef
Keyword(s):  
Remote Sensing ◽  
Systematic Review ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Sensor Technology ◽  
Future Research ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Impact

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

scholarly journals Portable snapshot spectral imaging for agriculture

2018 ◽  
pp. 221-225 ◽  
Author(s):  
András Jung ◽  
René Michels ◽  
Rainer Graser
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hyperspectral Imaging ◽  
Management Strategies ◽  
Variable Rate ◽  
Sensing Applications ◽  
Regular Variable ◽  
Remote Sensing Applications ◽  
Video Sensors ◽  
Areas Of Interest

High-resolution proximal and remote sensing applications can consolidate sustainable, prevention- and precision-oriented crop management strategies to decrease production risks. This paper shows significant perspectives, own developments and technical aspects of high resolution remote sensing in the context of field applications. Moreover, we provide an overview of snapshot hyperspectral imaging and potential field video sensors to identify areas of interest for their future development. One of the main conclusions of our paper is that non-scanning snapshot hyperspectral imaging technology may enable researchers to overcome the gap in the “point to image” transition of field sensing, while providing a flexible solution for regular variable-rate applications.

Efficient dimension reduction of hyperspectral images for big data remote sensing applications

2020 ◽  
Vol 14 (03) ◽  
pp. 1
Author(s):  
Beatriz P. Garcia-Salgado ◽  
Volodymyr I. Ponomaryov ◽  
Sergiy Sadovnychiy ◽  
Rogelio Reyes-Reyes
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Dimension Reduction ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

On the parallel classification system using hyperspectral images for remote sensing applications

2018 ◽  
Author(s):  
Volodymyr I. Ponomaryov ◽  
Beatriz P. Garcia-Salgado ◽  
Cesar M. A. Robles-Gonzalez ◽  
Sergiy Sadovnychiy
Keyword(s):  
Remote Sensing ◽  
Classification System ◽  
Hyperspectral Images ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4878
Author(s):  
Ivan Racetin ◽  
Andrija Krtalić
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Anomaly Detection ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Detection Methods ◽  
Hyperspectral Image Processing ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

scholarly journals Measurement of Environmentally Influenced Variations in Anthocyanin Accumulations in Brassica rapa subsp. Chinensis (Bok Choy) Using Hyperspectral Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Hyo-suk Kim ◽  
Ji Hye Yoo ◽  
Soo Hyun Park ◽  
Jun-Sik Kim ◽  
Youngchul Chung ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Brassica Rapa ◽  
Environmental Conditions ◽  
Imaging System ◽  
Plant Phenotyping ◽  
Growth Stages ◽  
Anthocyanin Accumulation ◽  
Potential Health ◽  
Destructive Analysis ◽  
Leaf Tissues

Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in “purple magic” and “red stem” by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in “red stem,” “white stem,” and “green stem.” Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.

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