Special Issue "Satellite Image Processing and Applications"

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

Deadline for manuscript submissions: 15 October 2020.

Special Issue Editors

Dr. Alireza Hamedianfar
Website
Guest Editor
Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
Interests: remote sensing; machine learning; deep learning; object-based image analysis; urban mapping; vegetation species detection
Prof. Dr. Petri Pellikka
Website
Guest Editor
Earth Change Observation Laboratory, Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
Interests: remote sensing; mapping; climate change; machine learning; vegetation species detection
Special Issues and Collections in MDPI journals
Assoc. Prof. Dr. Helmi Shafri
Website
Guest Editor
Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, Malaysia
Interests: remote sensing; environmental monitoring; machine learning; natural hazard assessment; impervious surface detection; vegetation species detection

Special Issue Information

Dear Colleagues,

Satellite remote sensing data has been rapidly used from a wide range of sensors and plays an important roles in earth surface material monitoring. Most of the optical satellite sensors provide multispectral bands and finer spatial resolution for panchromatic band. Landsat-8 and Sentinel2A/B data are among freely available satellite data. Landsat-8 Operational Land Imager (OLI) was launched on 2013 and has been improved compared with Landsat-7 Enhanced Thematic Mapper (ETM+) in calibration, signal to noise ratio, radiometric resolution and spectral wavebands. European Space Agency (ESA) launched Sentinel-2A and Sentinel-2B satellite sensors on 2015 and 2017, respectively; providing multispectral imagery in 13 spectral bands at different spatial resolutions (10 to 60 m). The commercially available very-high-resolution (VHR) sensors such as IKONOS, QuickBird, GeoEye-1, WorldView-2/3 and many other VHR satellites have contributed to finer/detailed characterization of earth surface features. Moreover, SAR imagery are also available from different sources such as Cosmo-Skymed, Sentinel-1 and TerraSAR-X, etc.

In consequence, the advancement in sensor technology and image processing algorithms enable the potential to develop novel methodologies and improve upon traditional processing methods in terms of cost, quantitative and qualitative accuracy, and objectivity. Satellite image processing may include wide spectrum of applications including imagery classification, multi-temporal image classification, multi-sensor data fusion, characterization of earth ecosystem processes and environmental monitoring, etc.

The goal of this special issue is to collect latest developments, methodologies and applications of satellite image data for remote sensing. We welcome submissions which provide the community with the most recent advancements on all aspects of satellite remote sensing processing and applications, including but not limited to:

  • Data fusion and integration of multi-sensor data
  • Image segmentation and classification algorithms
  • Feature selection algorithms
  • Machine learning techniques
  • Geographic Object-Based Image Analysis
  • Deep learning
  • Change detection and multi-temporal analysis
  • Urban mapping
  • Vegetation and species detection within complex environment
  • Impervious surface detection
  • Natural hazard assessment
Dr. Alireza Hamedianfar
Prof. Petri Pellikka
Assoc. Prof. Dr. Helmi Shafri
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. 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 2200 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

  • remote sensing applications
  • machine learning
  • image classification
  • optimization
  • image segmentation
  • neural networks
  • feature selection
  • computer vision

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Detection of Parking Cars in Stereo Satellite Images
Remote Sens. 2020, 12(13), 2170; https://doi.org/10.3390/rs12132170 - 07 Jul 2020
Abstract
In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details [...] Read more.
In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness. Full article
(This article belongs to the Special Issue Satellite Image Processing and Applications)
Show Figures

Graphical abstract

Open AccessArticle
An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning
Remote Sens. 2020, 12(13), 2106; https://doi.org/10.3390/rs12132106 - 01 Jul 2020
Abstract
Recent developments in hyperspectral satellites have dramatically promoted the wide application of large-scale quantitative remote sensing. As an essential part of preprocessing, cloud detection is of great significance for subsequent quantitative analysis. For Gaofen-5 (GF-5) data producers, the daily cloud detection of hundreds [...] Read more.
Recent developments in hyperspectral satellites have dramatically promoted the wide application of large-scale quantitative remote sensing. As an essential part of preprocessing, cloud detection is of great significance for subsequent quantitative analysis. For Gaofen-5 (GF-5) data producers, the daily cloud detection of hundreds of scenes is a challenging task. Traditional cloud detection methods cannot meet the strict demands of large-scale data production, especially for GF-5 satellites, which have massive data volumes. Deep learning technology, however, is able to perform cloud detection efficiently for massive repositories of satellite data and can even dramatically speed up processing by utilizing thumbnails. Inspired by the outstanding learning capability of convolutional neural networks (CNNs) for feature extraction, we propose a new dual-branch CNN architecture for cloud segmentation for GF-5 preview RGB images, termed a multiscale fusion gated network (MFGNet), which introduces pyramid pooling attention and spatial attention to extract both shallow and deep information. In addition, a new gated multilevel feature fusion module is also employed to fuse features at different depths and scales to generate pixelwise cloud segmentation results. The proposed model is extensively trained on hundreds of globally distributed GF-5 satellite images and compared with current mainstream CNN-based detection networks. The experimental results indicate that our proposed method has a higher F1 score (0.94) and fewer parameters (7.83 M) than the compared methods. Full article
(This article belongs to the Special Issue Satellite Image Processing and Applications)
Show Figures

Graphical abstract

Open AccessArticle
A Classified Adversarial Network for Multi-Spectral Remote Sensing Image Change Detection
Remote Sens. 2020, 12(13), 2098; https://doi.org/10.3390/rs12132098 - 30 Jun 2020
Abstract
Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the [...] Read more.
Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets. Full article
(This article belongs to the Special Issue Satellite Image Processing and Applications)
Show Figures

Graphical abstract

Back to TopTop