Special Issue "Computer Vision and Machine Learning Application on Earth Observation"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Dr. Juan Ignacio Arribas
E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Valladolid, Spain
Interests: computer-aided diagnosis; computer vision; machine learning; expert systems

Special Issue Information

Dear Colleagues,

With the rapid development of computing, the interest, power, and advantages of automatic computer-aided processing techniques in science and engineering have become clear—in particular, automatic computer vision (CV) techniques together with machine learning (ML, a.k.a. computational intelligence or machine intelligence) systems, in order to reach both a very high degree of automation and high accuracy. CV in conjunction with ML may be applied to a high number of problems of interest, such as in remote Earth sensing, mainly through different nature remote imaging and remote video processing approaches that have been made possible due to the very rapid development and growth of high-resolution, high-SNR, and low-cost imaging sensors and devices of various types, including single or multiple sensor, visible-range CCD/CMOS, hyper-spectral, multi-spectral, infrared, ultraviolet, and thermal, to name a few.

At the same time, it is clear how the use of autonomous ML systems—including expert systems, neural networks, and genetic algorithms, among others—has recently seen very rapid development, allowing computer-aided diagnosis, automatic classification, pattern recognition, and regression using ML techniques and learning algorithms with either supervised or unsupervised learning and reinforcement or deep learning paradigms.

Given the reasons above, the application of CV and ML to remote Earth observation and sensing is becoming highly attractive and popular, making it possible to reach a very high degree of autonomous functioning, accuracy, and promising results, including the following applications among others of interest:

  • Aerial imaging systems;
  • Agriculture field and aquaculture open-air automatic image classification systems;
  • Air traffic, airways, and plane pathways observation;
  • Climate and atmospheric/tropospheric observation, prediction, classification, and sensing systems;
  • Crops, crop yield, vegetation, and forest remote imaging sensing systems;
  • Deep-space star sensing;
  • Earth-surface remote sensing;
  • Earth-surface traffic, street, road and highway detection, classification, and sensing systems;
  • Ecology, eco-systems, wild life and migratory remote observation and monitoring;
  • Electrical power lines and power supply system remote imaging;
  • Fire detection and monitoring systems;
  • Hybrid automatic sensing systems;
  • Hyper-spectral imaging remote sensing systems;
  • Maritime/ship traffic observation, classification, or estimation;
  • Multi-sensor array remote sensing systems;
  • Multi-spectral automatic remote imaging systems;
  • Navigation, GPS, and other Earth-surface geodesic and localization systems;
  • Open-air orchard/vineyard imaging sensing;
  • Population, people, and crowd remote imaging estimation or counting;
  • Railway traffic lines remote observation;
  • Satellite Earth observation;
  • Storm, cloud, rainfall, and water diffraction sensing;
  • Time-lapse and seasonal Earth observation;
  • UAV/drone imaging systems;
  • Water, river, lake, sea, and flooding remote observation and monitoring.

Dr. Juan Ignacio Arribas
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 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 2400 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

  • classification
  • computer vision
  • detection and estimation
  • expert systems
  • imaging
  • learning systems
  • machine learning
  • neural networks
  • optimization
  • pattern recognition
  • receiver operating characteristic
  • remote sensing applications
  • segmentation
  • video processing

Published Papers (6 papers)

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

Research

Article
Deep Learning Based Electric Pylon Detection in Remote Sensing Images
Remote Sens. 2020, 12(11), 1857; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111857 - 08 Jun 2020
Cited by 1 | Viewed by 1033
Abstract
The working condition of power network can significantly influence urban development. Among all the power facilities, electric pylon has an important effect on the normal operation of electricity supply. Therefore, the work status of electric pylons requires continuous and real-time monitoring. Considering the [...] Read more.
The working condition of power network can significantly influence urban development. Among all the power facilities, electric pylon has an important effect on the normal operation of electricity supply. Therefore, the work status of electric pylons requires continuous and real-time monitoring. Considering the low efficiency of manual detection, we propose to utilize deep learning methods for electric pylon detection in high-resolution remote sensing images in this paper. To verify the effectiveness of electric pylon detection methods based on deep learning, we tested and compared the comprehensive performance of 10 state-of-the-art deep-learning-based detectors with different characteristics. Extensive experiments were carried out on a self-made dataset containing 1500 images. Moreover, 50 relatively complicated images were selected from the dataset to test and evaluate the adaptability to actual complex situations and resolution variations. Experimental results show the feasibility of applying deep learning methods to electric pylon detection. The comparative analysis can provide reference for the selection of specific deep learning model in actual electric pylon detection task. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Article
Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy
Remote Sens. 2020, 12(9), 1520; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091520 - 09 May 2020
Cited by 7 | Viewed by 1313
Abstract
Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest [...] Read more.
Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Article
Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies
Remote Sens. 2020, 12(7), 1110; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071110 - 31 Mar 2020
Cited by 4 | Viewed by 1097
Abstract
Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image [...] Read more.
Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Article
Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS)
by and
Remote Sens. 2020, 12(4), 672; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040672 - 18 Feb 2020
Cited by 2 | Viewed by 837
Abstract
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and [...] Read more.
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Article
Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation
Remote Sens. 2020, 12(4), 625; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040625 - 13 Feb 2020
Cited by 7 | Viewed by 1207
Abstract
Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network [...] Read more.
Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Article
Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery
Remote Sens. 2020, 12(1), 142; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010142 - 01 Jan 2020
Cited by 5 | Viewed by 998
Abstract
In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the [...] Read more.
In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
Show Figures

Graphical abstract

Back to TopTop