Special Issue "Innovative Sensing - From Sensors to Methods and Applications"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (3 February 2019)

Special Issue Editors

Guest Editor
Professor Boris Jutzi

Adjunct Professor, Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, Karlsruhe, Germany
Website | E-Mail
Interests: Active Sensors, Computer Vision, Laserscanning, Optical Measurement Technology, Signal & Image Processing
Guest Editor
Dr. Martin Weinmann

Assistant Professor, Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, Karlsruhe, Germany
Website | E-Mail
Interests: Computer Vision, Pattern Recognition, Machine Learning, 3D Vision, Laser Scanning
Guest Editor
Dr. Raul Queiroz Feitosa

Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, r. Marquês de São Vicente, 225, 22451-900, Rio de Janeiro, RJ, Brazil
Website | E-Mail
Phone: +55 21 35271212
Fax: +55 21 35271232
Interests: pattern recognition for remote sensing; image analysis; remote sensing applications
Guest Editor
Professor Stefan Hinz

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, Karlsruhe, Germany
Website | E-Mail
Interests: Image Analysis, Remote Sensing, Computer Vision, Digital Image Processing, Pattern Recognition, Machine Learning

Special Issue Information

Dear Colleagues,

 

Recent years have been characterized by rapid developments in various fields of sensor technology, design of smart sensor networks, unmanned platforms and new satellite imaging concepts or satellite constellations, respectively. This includes the sector of–often low-cost–industrial imaging sensors and, likewise, the development of highly sophisticated and specialized sensors for Earth Observation, thereby covering multiple modes of active or passive sensor technology and various scales of imaging.

 

With this Special Issue on "Innovative Sensing—From Sensors to Methods and Applications" we address research methods as well as applications on the design, construction, characterization, calibration and use of imaging and non-imaging sensors, sensor systems and sensor networks for photogrammetry, remote sensing and spatial information science. This includes the development of new and innovative technological concepts, likewise, models and methods to optimally exploit, calibrate and thoroughly evaluate new sensors, networks and single sensor components.

 

Prospective authors are cordially invited to contribute to this Special Issue by submitting an article containing original research.

 

Prof. Dr. Boris Jutzi
Dr. Martin Weinmann
Prof. Dr. Raul Feitosa
Prof. Dr. Stefan Hinz
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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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.

Published Papers (4 papers)

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Research

Open AccessArticle On the Feasibility of Water Surface Mapping with Single Photon LiDAR
ISPRS Int. J. Geo-Inf. 2019, 8(4), 188; https://doi.org/10.3390/ijgi8040188
Received: 9 February 2019 / Revised: 25 March 2019 / Accepted: 1 April 2019 / Published: 10 April 2019
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Abstract
Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable [...] Read more.
Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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Open AccessArticle Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping
ISPRS Int. J. Geo-Inf. 2019, 8(4), 179; https://doi.org/10.3390/ijgi8040179
Received: 3 January 2019 / Revised: 2 April 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
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Abstract
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics [...] Read more.
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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Open AccessArticle Bangkok CCTV Image through a Road Environment Extraction System Using Multi-Label Convolutional Neural Network Classification
ISPRS Int. J. Geo-Inf. 2019, 8(3), 128; https://doi.org/10.3390/ijgi8030128
Received: 30 January 2019 / Accepted: 24 February 2019 / Published: 4 March 2019
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Abstract
Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been [...] Read more.
Information regarding the conditions of roads is a safety concern when driving. In Bangkok, public weather sensors such as weather stations and rain sensors are insufficiently available to provide such information. On the other hand, a number of existing CCTV cameras have been deployed recently in various places for surveillance and traffic monitoring. Instead of deploying new sensors designed specifically for monitoring road conditions, images and location information from existing cameras can be used to obtain precise environmental information. Therefore, we propose a road environment extraction framework that covers different situations, such as raining and non-raining scenes, daylight and night-time scenes, crowded and non-crowded traffic, and wet and dry roads. The framework is based on CCTV images from a Bangkok metropolitan dataset, provided by the Bangkok Metropolitan Administration. To obtain information from CCTV image sequences, multi-label classification was considered by applying a convolutional neural network. We also compared various models, including transfer learning techniques, and developed new models in order to obtain optimum results in terms of performance and efficiency. By adding dropout and batch normalization techniques, our model could acceptably perform classification with only a few convolutional layers. Our evaluation showed a Hamming loss and exact match ratio of 0.039 and 0.84, respectively. Finally, a road environment monitoring system was implemented to test the proposed framework. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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Open AccessArticle Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery
ISPRS Int. J. Geo-Inf. 2019, 8(1), 47; https://doi.org/10.3390/ijgi8010047
Received: 14 December 2018 / Revised: 15 January 2019 / Accepted: 16 January 2019 / Published: 18 January 2019
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Abstract
The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In [...] Read more.
The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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