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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 39709

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Special Issue Editors

Dr. Sebastian Lopez

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Guest Editor

Institute for Applied Microelectronics at the University of Las Palmas de Gran Canaria, Edificio de Electronica y Telecomunicaciones, Campus Universitario de Tafira, 35017 Las Palmas de Gran Canaria, Spain
Interests: hyperspectral image analysis and compression; high-performance computing; real-time systems

Prof. Dr. Bing Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; dynamic monitoring of global resource environment with remote sensing; intelligent interpretation of remotely sensed big data
Special Issues, Collections and Topics in MDPI journals

Dr. Bormin Huang

E-Mail Website
Guest Editor

Space Science and Engineering Center, University of Wisconsin-Madison, 1225 W. Dayton St, Madison, WI 53706, USA
Interests: satellite data compression; high-performance computing in remote sensing; remote sensing image processing; remote sensing forward modeling and inverse problems
Special Issues, Collections and Topics in MDPI journals

Dr. Lucana Santos

E-Mail Website
Guest Editor

Moltek Ltd for European Space Agency, Keplerlaan, 1, 2201 AZ Noordwijk
Interests: satellite image compression; hyperspectral imaging; design methodologies; hardware modelling; FPGAs; GPUs

Prof. Dr. Jun Li

E-Mail Website
Guest Editor

School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: remote sensing image processing; data fusion; hyperspectral image classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thanks to the extraordinary technological advances in the imaging field, nowadays remote sensors provide an extraordinarily rich amount of information about the scenes that they capture from unmanned aerial vehicles (UAVs), airplanes, Earth Observation satellites or spacecraft. However, the implications of the wealth of information available is the massive amount of data that these instruments generate, which allows us to enhance the information content, but also uncovers significant challenges for applications for which it is crucial to process the acquired images in real-time. This clearly leads to a highly controversial situation, in the sense that the same factor (the formidable amount of information) becomes at the same time positive (it allows the achievement of better and more precise results) and negative (it takes more time to process the data) for the same group of time-critical applications. Hence, the real-time processing, analysis, and compression of remotely-sensed imaging data are highly desired to support fast decision-making, which requires new developments and improvements in hardware and software that are highly driven by the applications, scanning modes, platforms, packaging and systems.

This Special Issue of Remote Sensing is devoted to presenting state-of-the-art research on the real-time, or near-real-time processing of imaging data (including, among others, multispectral, hyperspectral, ultraspectral, SAR, LiDAR, PolSAR) captured from remote sensing platforms. Papers are solicited on, but not limited to, the following research topics:

Low computational complexity and hardware-friendly algorithms for the real-time processing, analysis and/or compression of remotely-sensed images.
Hardware/software embedded systems for on-board real-time processing, analysis and/or compression of remotely-sensed images.
Fault tolerance, reconfigurability, low power and other techniques especially relevant for on-board satellite imaging systems.
Utilization of on-ground high performance computing (HPC) facilities for the real-time processing, analysis and/or compression of remotely-sensed images.
Hardware/software design methodologies and design flows for the efficient development of real-time systems for the processing, analysis and/or compression of remotely-sensed images.
Tools and virtual environments for the validation and verification of complex systems devoted to the real-time processing, analysis and/or compression of remotely-sensed images.
Application-oriented hardware/software systems for the successful real-time processing, analysis and/or compression of remotely-sensed images.
Big-data in remote sensing.
Artificial intelligence techniques for the real-time processing, analysis and/or compression of remotely-sensed images.

Prof. Dr. Sebastian Lopez
Prof. Dr. Bing Zhang
Dr. Bormin Huang
Dr. Lucana Santos
Prof. Dr. Jun Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Real-time processing and compression
Low complexity algorithms
Hardware/software implementation
High-performance and parallel computing
On-board image processing systems
Design methodologies and tools
Validation and verification methodologies
Applications
Big data
Artificial intelligence

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Published Papers (8 papers)

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Research

23 pages, 18542 KiB

Open AccessArticle

Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation

by Jorge Martínez Sánchez, Álvaro Váquez Álvarez, David López Vilariño, Francisco Fernández Rivera, José Carlos Cabaleiro Domínguez and Tomás Fernández Pena

Remote Sens. 2019, 11(19), 2256; https://doi.org/10.3390/rs11192256 - 27 Sep 2019

Cited by 10 | Viewed by 5067

Abstract

Over the last two decades, a wide range of applications have been developed from Light Detection and Ranging (LiDAR) point clouds. Most LiDAR-derived products require the distinction between ground and non-ground points. Because of this, ground filtering its being one of the most studied topics in the literature and robust methods are nowadays available. However, these methods have been designed to work with offline data and they are generally not well suited for real-time scenarios. Aiming to address this issue, this paper proposes an efficient method for ground filtering of airborne LiDAR data based on scan-line processing. In our proposal, an iterative 1-D spline interpolation is performed in each scan line sequentially. The final spline knots of a scan line are taken into account for the next scan line, so that valuable 2-D information is also considered without compromising computational efficiency. Points are labelled into ground and non-ground by analysing their residuals to the final spline. When tested against synthetic ground truth, the method yields a mean kappa value of 88.59% and a mean total error of 0.50%. Experiments with real data also show satisfactory results under visual inspection. Performance tests on a workstation show that the method can process up to 1 million points per second. The original implementation was ported into a low-cost development board to demonstrate its feasibility to run in embedded systems, where throughput was improved by using programmable logic hardware acceleration. Analysis shows that real-time filtering is possible in a high-end board prototype, as it can process the amount of points per second that current lightweight scanners acquire with low-energy consumption. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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20 pages, 5729 KiB

Open AccessArticle

A Simulation Environment for Validation and Verification of Real Time Hyperspectral Processing Algorithms on-Board a UAV

by Pablo Horstrand, José Fco. López, Sebastián López, Tapio Leppälampi, Markku Pusenius and Martijn Rooker

Remote Sens. 2019, 11(16), 1852; https://doi.org/10.3390/rs11161852 - 9 Aug 2019

Cited by 1 | Viewed by 3942

Abstract

The utilization of hyperspectral imaging sensors has gained a significant relevance among many different applications due to their capability for collecting a huge amount of information across the electromagnetic spectrum. These sensors have been traditionally mounted on-board satellites and airplanes in order to extract information from the Earth’s surface. Fortunately, the progressive miniaturization of these sensors during the last lustrum has enabled their use in other remote sensing platforms, such as drones equipped with hyperspectral cameras which bring advantages in terms of higher spatial resolution of the acquired images, more flexible revisit times and lower cost of the flight campaigns. However, when these drones are autonomously flying and taking real-time critical decisions from the information contained in the captured images, it is crucial that the whole process takes place in a safe and predictable manner. In order to deal with this problem, a simulation environment is presented in this work to analyze the virtual behavior of a drone equipped with a pushbroom hyperspectral camera used for assisting harvesting applications, which enables an exhaustive and realistic validation and verification of the drone real-time hyperspectral imaging system prior to its launch. To the best of the authors’ knowledge, the proposed environment represents the only solution in the state-of-the-art that allows the virtual verification of real-time hyperspectral image processing algorithms under realistic conditions. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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14 pages, 17182 KiB

Open AccessArticle

Convolutional Neural Networks for On-Board Cloud Screening

by Sina Ghassemi and Enrico Magli

Remote Sens. 2019, 11(12), 1417; https://doi.org/10.3390/rs11121417 - 14 Jun 2019

Cited by 17 | Viewed by 4962

Abstract

A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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20 pages, 7156 KiB

Open AccessArticle

A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs

by Weijia Li, Conghui He, Haohuan Fu, Juepeng Zheng, Runmin Dong, Maocai Xia, Le Yu and Wayne Luk

Remote Sens. 2019, 11(9), 1025; https://doi.org/10.3390/rs11091025 - 30 Apr 2019

Cited by 28 | Viewed by 4835

Abstract

The on-board real-time tree crown detection from high-resolution remote sensing images is beneficial for avoiding the delay between data acquisition and processing, reducing the quantity of data transmission from the satellite to the ground, monitoring the growing condition of individual trees, and discovering the damage of trees as early as possible, etc. Existing high performance platform based tree crown detection studies either focus on processing images in a small size or suffer from high power consumption or slow processing speed. In this paper, we propose the first FPGA-based real-time tree crown detection approach for large-scale satellite images. A pipelined-friendly and resource-economic tree crown detection algorithm (PF-TCD) is designed through reconstructing and modifying the workflow of the original algorithm into three computational kernels on FPGAs. Compared with the well-optimized software implementation of the original algorithm on an Intel 12-core CPU, our proposed PF-TCD obtains the speedup of 18.75 times for a satellite image with a size of 12,188 × 12,576 pixels without reducing the detection accuracy. The image processing time for the large-scale remote sensing image is only 0.33 s, which satisfies the requirements of the on-board real-time data processing on satellites. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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15 pages, 1346 KiB

Open AccessArticle

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

by Bruno Aiazzi, Massimo Selva, Alberto Arienzo and Stefano Baronti

Remote Sens. 2019, 11(7), 791; https://doi.org/10.3390/rs11070791 - 2 Apr 2019

Cited by 9 | Viewed by 3778

Abstract

A noticeable topic to be pursued in the field of on-board real-time data processing is the influence of the modulation transfer function (MTF) of the image acquisition system on the lossless compressibility of raw (that is, uncalibrated) hyperspectral data. Actually, notwithstanding the system device is constrained by several design and manufacturing requirements, the impact of the on-board MTF on the performance of data compressors is becoming remarkable. In particular, the aim of reducing both transmission bandwidth/power and mass storage can be efficiently pursued. Such an analysis is expected to be useful especially for systems employed in mini-satellites, whose payload must be compact and light. From this perspective, this paper investigates the performance of a typical imaging system that acquires low/medium-spatial-resolution images, by considering high-resolution reference data, which simulate the real scene to be imaged. To this end, standard Consultative Committee for Space Data Systems (CCSDS) Aviris 2006 data have been chosen, due to their spatial resolution of

17

m, which is adequate to be a reference for simulated data whose spatial resolution is foreseen between

50

and

150

m. MTF requirements are usually provided based on the cut-off value of the amplitude at the Nyquist frequency, which is defined as a half of the sampling frequency. Typically, a cut-off value between

0.2

and

0.3

ensures that a sufficient amount of information is delivered from the scene to the acquired image, by avoiding at the same time the degradation due to an excessive aliasing distortion. All the scores are achieved by running the standard lossless compression scheme CCSDS 1.2.3.0-B-1 for multispectral/hyperspectral data, as a function of the cut-off value and different noise acquisition levels. The final results, and related plots, show that this analysis can suggest a suitable choice for the cut-off value, to ensure both a sufficient quality and low bit rates for the transmitted data to the ground station. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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19 pages, 755 KiB

Open AccessArticle

A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

by Milica Orlandić, Johan Fjeldtvedt and Tor Arne Johansen

Remote Sens. 2019, 11(6), 673; https://doi.org/10.3390/rs11060673 - 21 Mar 2019

Cited by 34 | Viewed by 5108

Abstract

Satellite onboard processing for hyperspectral imaging applications is characterized by large data sets, limited processing resources and limited bandwidth of communication links. The CCSDS-123 algorithm is a specialized compression standard assembled for space-related applications. In this paper, a parallel FPGA implementation of CCSDS-123 compression algorithm is presented. The proposed design can compress any number of samples in parallel allowed by resource and I/O bandwidth constraints. The CCSDS-123 processing core has been placed on Zynq-7035 SoC and verified against the existing reference software. The estimated power use scales approximately linearly with the number of samples processed in parallel. Finally, the proposed implementation outperforms the state-of-the-art implementations in terms of both throughput and power. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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28 pages, 18032 KiB

Open AccessArticle

On-Board Georeferencing Using FPGA-Based Optimized Second-Order Polynomial Equation

by Dequan Liu, Guoqing Zhou, Jingjin Huang, Rongting Zhang, Lei Shu, Xiang Zhou and Chun Sheng Xin

Remote Sens. 2019, 11(2), 124; https://doi.org/10.3390/rs11020124 - 10 Jan 2019

Cited by 18 | Viewed by 4966

Abstract

For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable gate array (FPGA) can meet the requirements of design. In the coordinate transformation scheme, 250,656 LUTs, 499,268 registers, and 388 DSP48s are used. Furthermore, 27,218 LUTs, 45,823 registers, 456 RAM/FIFO, and 267 DSP48s are used in the bilinear interpolation module; (3) the values of root mean square errors (RMSEs) are less than one pixel, and the other statistics, such as maximum error, minimum error, and mean error are less than one pixel; (4) the gray values of the georeferenced image when implemented using FPGA have the same accuracy as those implemented using MATLAB and Visual studio (C++), and have a very close accuracy implemented using ENVI software; and (5) the on-chip power consumption is 0.659 W. Therefore, it can be concluded that the proposed georeferencing method implemented using FPGA with second-order polynomial model and bilinear interpolation algorithm can achieve real-time geographic referencing for remotely sensed imagery. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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21 pages, 3337 KiB

Open AccessArticle

A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images

by Alberto Ortiz, Alfonso Rodríguez, Raúl Guerra, Sebastián López, Andrés Otero, Roberto Sarmiento and Eduardo De la Torre

Remote Sens. 2018, 10(11), 1790; https://doi.org/10.3390/rs10111790 - 12 Nov 2018

Cited by 3 | Viewed by 4390

Abstract

Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo³ framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo³-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth. Full article

(This article belongs to the Special Issue Real-Time Processing of Remotely-Sensed Imaging Data)

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