New Computational Models for Image Remote Sensing and Big Data

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch012 ◽

2019 ◽

pp. 232-248

Author(s):

Dhanasekaran K. Pillai

Keyword(s):

Remote Sensing ◽

Big Data ◽

Computational Models ◽

Image Data ◽

Wireless Sensor ◽

Data Handling ◽

Sensing Applications ◽

Emerging Trends ◽

Remote Sensing Applications ◽

Handling Method

This chapter focuses on the development of new computational models for remote sensing applications with big data handling method using image data. Furthermore, this chapter presents an overview of the process of developing systems for remote sensing and monitoring. The issues and challenges are presented to discuss various problems related to the handling of image big data in wireless sensor networks that have various real-world applications. Moreover, the possible solutions and future recommendations to address the challenges have been presented and also this chapter includes discussion of emerging trends and a conclusion.

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New Computational Models for Image Remote Sensing and Big Data

Big Data Analytics for Satellite Image Processing and Remote Sensing - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-3643-7.ch001 ◽

2018 ◽

pp. 1-21

Author(s):

Dhanasekaran K. Pillai

Keyword(s):

Remote Sensing ◽

Big Data ◽

Computational Models ◽

Image Data ◽

Wireless Sensor ◽

Data Handling ◽

Sensing Applications ◽

Emerging Trends ◽

Remote Sensing Applications ◽

Handling Method

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A review on real-time big data analysis in remote sensing applications

2017 25th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2017.7960499 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mustafa Kemal Pekturk ◽

Muhammet Unal

Keyword(s):

Remote Sensing ◽

Big Data ◽

Data Analysis ◽

Real Time ◽

Big Data Analysis ◽

Sensing Applications ◽

Remote Sensing Applications

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

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.14.032611 ◽

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

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IFRAD: A Fast Feature Descriptor for Remote Sensing Images

Remote Sensing ◽

10.3390/rs13183774 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3774

Author(s):

Qinping Feng ◽

Shuping Tao ◽

Chunyu Liu ◽

Hongsong Qu ◽

Wei Xu

Keyword(s):

Remote Sensing ◽

Feature Matching ◽

State Of The Art ◽

Image Data ◽

Feature Descriptor ◽

Feature Description ◽

Sensing Applications ◽

Feature Similarity ◽

Feature Based ◽

Remote Sensing Applications

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

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