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Object Detection from Aerial and Space Platforms Using Deep Learning Methods

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

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 25895

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Special Issue Editors

Dr. Antonio Pertusa

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Guest Editor

Department of Software and Computing Systems, Computer Science Research Institute, University of Alicante, Carretera San Vicente s/n, San Vicente del Raspeig, E-03690 Alicante, Spain
Interests: deep learning; remote sensing; object detection; signal processing; machine learning

Prof. Dr. Pablo Gil

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Guest Editor

Computer Science Research Institute/Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain
Interests: robotic manipulation; robotic grasping; tactile/visual perception; robot vision; object recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Antonio-Javier Gallego

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Guest Editor

Department of Software and Computing Systems, University of Alicante, E-03080 Alicante, Spain
Interests: machine learning; pattern recognition; computer vision; deep learning

Special Issue Information

Dear Colleagues,

Object detection is very important for a wide scope of remote sensing applications, such as intelligent monitoring, urban planning, precision agriculture, vegetation supervision, urban, rescue operations, and environmental survey tasks.
Since the rise of deep neural networks, especially convolutional neural networks, an increasing number of techniques have been proposed to reliably detect objects both on Earth’s surface (airports, buildings, ships, cars, vegetation, people, etc.) and space (stars, debris, satellites, etc.). To address these tasks, data captured with optical, hyperspectral, thermal, lidar, SAR, or multispectral sensors are normally used.
This Special Issue will collect new developments and deep neural network methodologies, datasets, and applications for object detection using remote sensing data. We welcome submissions that provide the most recent advancements in all aspects of object detection and identification from aerial and space platforms, including but not limited to the following:

Object detection methods.
Object change detection and monitoring methods.
High-quality datasets for object detection and identification.
Transfer learning methods.
Image segmentation methods.
Object detection and identification using multi-source and multi-modal data.
Similarity search methods.
Space object detection and recognition.
Embedded intelligent computer vision algorithms.

Dr. Antonio Pertusa
Dr. Pablo Gil Vázquez
Dr. Antonio-Javier Gallego
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

object detection
image segmentation
deep learning
transfer learning
computer vision
aerial and space imaging

Published Papers (7 papers)

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19 pages, 163203 KiB

Open AccessArticle

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

by Pengfei Shi, Qigang Jiang, Chao Shi, Jing Xi, Guofang Tao, Sen Zhang, Zhenchao Zhang, Bin Liu, Xin Gao and Qian Wu

Remote Sens. 2021, 13(16), 3243; https://doi.org/10.3390/rs13163243 - 16 Aug 2021

Cited by 16 | Viewed by 2947

Abstract

Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale and high-resolution remote sensing images, this article proposes an improved algorithm based on YOLO v4 with sliding slices and discarding edges. The algorithm effectively solves the problems of repeated detection and inaccurate positioning of oil well detection in large-scale and high-resolution remote sensing images, and the accuracy of detection result increases considerably. In summary, this study investigates an appropriate algorithm for oil well detection, improves the algorithm, and achieves an excellent effect on a large-scale and high-resolution satellite image. It provides a new idea for small objects detection in large-scale and high-resolution remote sensing images. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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21 pages, 1592 KiB

Open AccessArticle

Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

by Zheng He, Li Huang, Weijiang Zeng, Xining Zhang, Yongxin Jiang and Qin Zou

Remote Sens. 2021, 13(16), 3182; https://doi.org/10.3390/rs13163182 - 11 Aug 2021

Cited by 6 | Viewed by 2309

Abstract

The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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25 pages, 25925 KiB

Open AccessArticle

FastAER Det: Fast Aerial Embedded Real-Time Detection

by Stefan Wolf, Lars Sommer and Arne Schumann

Remote Sens. 2021, 13(16), 3088; https://doi.org/10.3390/rs13163088 - 05 Aug 2021

Cited by 6 | Viewed by 2565

Abstract

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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25 pages, 36080 KiB

Open AccessArticle

Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

by Pedro Zamboni, José Marcato Junior, Jonathan de Andrade Silva, Gabriela Takahashi Miyoshi, Edson Takashi Matsubara, Keiller Nogueira and Wesley Nunes Gonçalves

Remote Sens. 2021, 13(13), 2482; https://doi.org/10.3390/rs13132482 - 25 Jun 2021

Cited by 20 | Viewed by 4445

Abstract

Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an

A P_{50}

of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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27 pages, 6725 KiB

Open AccessArticle

Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning

by Filippo Maria Bianchi, Martine M. Espeseth and Njål Borch

Remote Sens. 2020, 12(14), 2260; https://doi.org/10.3390/rs12142260 - 14 Jul 2020

Cited by 50 | Viewed by 5362

Abstract

We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories of its shape and texture characteristics. The classification results provide valuable insights for improving the design of services for oil spill monitoring by world-leading providers. Finally, we present our operational pipeline and a visualization tool for large-scale data, which allows detection and analysis of the historical occurrence of oil spills worldwide. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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14 pages, 37862 KiB

Open AccessTechnical Note

Classification and Identification of Spectral Pixels with Low Maritime Occupancy Using Unsupervised Machine Learning

by Dongmin Seo, Sangwoo Oh and Daekyeom Lee

Remote Sens. 2022, 14(8), 1828; https://doi.org/10.3390/rs14081828 - 11 Apr 2022

Viewed by 1643

Abstract

For marine accidents, prompt actions to minimize the casualties and loss of property are crucial. Remote sensing using satellites or aircrafts enables effective monitoring over a large area. Hyperspectral remote sensing allows the acquisition of high-resolution spectral information. This technology detects target objects by analyzing the spectrum for each pixel. We present a clustering method of seawater and floating objects by analyzing aerial hyperspectral images. For clustering, unsupervised learning algorithms of K-means, Gaussian Mixture, and DBSCAN are used. The detection performance of those algorithms is expressed as the precision, recall, and F1 Score. In addition, this study presents a color mapping method that analyzes the detected small object using cosine similarity. This technology can minimize future casualties and property loss by enabling rapid aircraft and maritime search, ocean monitoring, and preparations against marine accidents. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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13 pages, 2605 KiB

Open AccessLetter

Single-Stage Rotation-Decoupled Detector for Oriented Object

by Bo Zhong and Kai Ao

Remote Sens. 2020, 12(19), 3262; https://doi.org/10.3390/rs12193262 - 08 Oct 2020

Cited by 24 | Viewed by 4736

Abstract

Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in the number of anchors and the sensitivity of the intersection over union (IoU) to changes of angle. To overcome these shortcomings while taking advantage of the oriented bounding boxes, we propose a novel rotation detector which redesigns the matching strategy between oriented anchors and ground truth boxes. The main idea of the new strategy is to decouple the rotating bounding box into a horizontal bounding box during matching, thereby reducing the instability of the angle to the matching process. Extensive experiments on public remote sensing datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that the proposed approach achieves state-of-the-art detection accuracy with higher efficiency. Full article

(This article belongs to the Special Issue Object Detection from Aerial and Space Platforms Using Deep Learning Methods)

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Object Detection from Aerial and Space Platforms Using Deep Learning Methods

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