Estimation of trace vapor concentration pathlength in plumes for remote sensing applications from hyperspectral images

2003 ◽  
Author(s):  
Neal B. Gallagher ◽  
David M. Sheen ◽  
Jeremy M. Shaver ◽  
Barry M. Wise ◽  
John F. Shultz
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Images ◽  
Vapor Concentration ◽  
Sensing Applications ◽  
Remote Sensing Applications

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.

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 PIECEWISE ANOMALY DETECTION USING MINIMAL LEARNING MACHINE FOR HYPERSPECTRAL IMAGES

2021 ◽  
Vol V-3-2021 ◽  
pp. 89-96
Author(s):  
A-M. Raita-Hakola ◽  
I. Pölönen
Keyword(s):  
Remote Sensing ◽  
Anomaly Detection ◽  
Real Time ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Computationally Efficient ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Learning Machine ◽  
Minimal Learning Machine

Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.

scholarly journals Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 353 ◽  
Author(s):  
Chiman Kwan
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Target Detection ◽  
Recent Change ◽  
Hyperspectral Images ◽  
Research Attention ◽  
Advantages And Disadvantages ◽  
Sensing Applications ◽  
Challenges And Opportunities ◽  
Remote Sensing Applications

Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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