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Open Resources in Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 17986

Special Issue Editor


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Guest Editor
1. Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169007 Porto, Portugal
2. Institute of Earth Sciences (ICT)-Porto Pole, University of Porto, 4169007 Porto, Portugal
Interests: remote sensing; image processing; environmental applications; geologic applications; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geo-information, more specifically remote sensing (RS) data, is crucial for several areas. The impact of access to open source software and open data in RS area is extraordinary. The access to free data and the ability to process these data in open source software has led to significant advances in several areas that depend on Earth observation data.

Open resources in RS include the acquisition, processing and analysis of open data and also data policy of RS data and data products. The free access to RS data is an added-value in the understanding of the Earth’s ecosystems, and in the understanding the land and ocean’s processes. Several plug-ins and scripts were made available in the last years to convert raw data in new products/services. Ocean color, sea surface height, sea surface wind and sea surface temperature were the areas that most contributed.

Important open data resources (remote sensing databases) should also be focused. The evolution of free and open remote sensing data access policy is also expected to be addressed.

Another topic with great interest is how to share open RS data using spatial data infrastructures (SDIs).

The following topics are considered for this Special Issue (but not limited to):

- Open data in RS;

- Development of software packages for RS data processing;

- Developments of new algorithms regarding RS data acquisition, analysis and visualization (radiometric correction, atmospheric correction, segmentation, classification, accuracy assessment, etc.)

- Development of scripts/plug-ins to convert raw data into new products/services;

- Development and utilization of virtual infrastructures for RS data sharing, integration, visualization and analysis;

- Open source solutions and open standards, specifications of RS data;

- Thematic Exploitation Platforms (open data and services).

We look forward to your contributions.

Prof. Ana Cláudia Teodoro
Guest Editor

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

  • open data
  • open source software
  • spatial data infrastructures
  • image computing
  • algorithms development

Published Papers (3 papers)

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22 pages, 9383 KiB  
Article
Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region
by Cláudia M. Viana, Inês Girão and Jorge Rocha
Remote Sens. 2019, 11(9), 1104; https://doi.org/10.3390/rs11091104 - 9 May 2019
Cited by 82 | Viewed by 9244
Abstract
The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set [...] Read more.
The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years. Full article
(This article belongs to the Special Issue Open Resources in Remote Sensing)
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18 pages, 9014 KiB  
Article
Improving Details of Building Façades in Open LiDAR Data Using Ground Images
by Shenman Zhang, Pengjie Tao, Lei Wang, Yaolin Hou and Zhihua Hu
Remote Sens. 2019, 11(4), 420; https://doi.org/10.3390/rs11040420 - 18 Feb 2019
Cited by 2 | Viewed by 3850
Abstract
Recent open data initiatives allow free access to a vast amount of light detection and ranging (LiDAR) data in many cities. However, most open LiDAR data of cities are acquired by airborne scanning, where points on building façades are sparse or even completely [...] Read more.
Recent open data initiatives allow free access to a vast amount of light detection and ranging (LiDAR) data in many cities. However, most open LiDAR data of cities are acquired by airborne scanning, where points on building façades are sparse or even completely missing due to occlusions in the urban environment, leading to the absence of façade details. This paper presents an approach for improving the LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two-point clouds of different sources with very limited overlaps. First, the façade point cloud generated from ground images is leveled by adjusting the facade normal to perpendicular to the upright direction. Then leveling façade point cloud is geolocated by alignment between images GPS data and their structure from motion (SfM) coordinates. Next, a modified coherent point drift algorithm with (surface) normal consistency is proposed to accurately align the façade point cloud to the LiDAR data. The significance of this work resides in the use of 2D overlapping points on the building outlines instead of the limited 3D overlap between the two-point clouds. This way we can still achieve reliable and precise registration under incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façade details in open LiDAR data, and achieve 2 to 10 times higher registration accuracy, when compared to classic registration methods. Full article
(This article belongs to the Special Issue Open Resources in Remote Sensing)
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11 pages, 3853 KiB  
Letter
An Iterative Coarse-to-Fine Sub-Sampling Method for Density Reduction of Terrain Point Clouds
by Lei Fan and Peter M. Atkinson
Remote Sens. 2019, 11(8), 947; https://doi.org/10.3390/rs11080947 - 19 Apr 2019
Cited by 10 | Viewed by 3937
Abstract
Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the [...] Read more.
Point clouds obtained from laser scanning techniques are now a standard type of spatial data for characterising terrain surfaces. Some have been shared as open data for free access. A problem with the use of these free point cloud data is that the data density may be more than necessary for a given application, leading to higher computational cost in subsequent data processing and visualisation. In such cases, to make the dense point clouds more manageable, their data density can be reduced. This research proposes a new coarse-to-fine sub-sampling method for reducing point cloud data density, which honours the local surface complexity of a terrain surface. The method proposed is tested using four point clouds representing terrain surfaces with distinct spatial characteristics. The effectiveness of the iterative coarse-to-fine method is evaluated and compared against several benchmarks in the form of typical sub-sampling methods available in open source software for point cloud processing. Full article
(This article belongs to the Special Issue Open Resources in Remote Sensing)
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