WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming

We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.Comment: 25 pages, 14 figures, MDPI Remote Sensin

Deep Learning based Vehicle Detection in Aerial Imagery

Der Einsatz von luftgestützten Plattformen, die mit bildgebender Sensorik ausgestattet sind, ist ein wesentlicher Bestandteil von vielen Anwendungen im Bereich der zivilen Sicherheit. Bekannte Anwendungsgebiete umfassen unter anderem die Entdeckung verbotener oder krimineller Aktivitäten, Verkehrsüberwachung, Suche und Rettung, Katastrophenhilfe und Umweltüberwachung. Aufgrund der großen Menge zu verarbeitender Daten und der daraus resultierenden kognitiven Überbelastung ist jedoch eine Analyse der Luftbilddaten ausschließlich durch menschliche Auswerter in der Praxis nicht anwendbar. Zur Unterstützung der menschlichen Auswerter kommen daher in der Regel automatische Bild- und Videoverarbeitungsalgorithmen zum Einsatz. Eine zentrale Aufgabe bildet dabei eine zuverlässige Detektion relevanter Objekte im Sichtfeld der Kamera, bevor eine Interpretation der gegebenen Szene stattfinden kann. Die geringe Bodenauflösung aufgrund der großen Distanz zwischen Kamera und Erde macht die Objektdetektion in Luftbilddaten zu einer herausfordernden Aufgabe, welche durch Bewegungsunschärfe, Verdeckungen und Schattenwurf zusätzlich erschwert wird. Obwohl in der Literatur eine Vielzahl konventioneller Ansätze zur Detektion von Objekten in Luftbilddaten existiert, ist die Detektionsgenauigkeit durch die Repräsentationsfähigkeit der verwendeten manuell entworfenen Merkmale beschränkt. Im Rahmen dieser Arbeit wird ein neuer Deep-Learning basierter Ansatz zur Detektion von Objekten in Luftbilddaten präsentiert. Der Fokus der Arbeit liegt dabei auf der Detektion von Fahrzeugen in Luftbilddaten, die senkrecht von oben aufgenommen wurden. Grundlage des entwickelten Ansatzes bildet der Faster R-CNN Detektor, der im Vergleich zu anderen Deep-Learning basierten Detektionsverfahren eine höhere Detektionsgenauigkeit besitzt. Da Faster R-CNN wie auch die anderen Deep-Learning basierten Detektionsverfahren auf Benchmark Datensätzen optimiert wurden, werden in einem ersten Schritt notwendige Anpassungen an die Eigenschaften der Luftbilddaten, wie die geringen Abmessungen der zu detektierenden Fahrzeuge, systematisch untersucht und daraus resultierende Probleme identifiziert. Im Hinblick auf reale Anwendungen sind hier vor allem die hohe Anzahl fehlerhafter Detektionen durch fahrzeugähnliche Strukturen und die deutlich erhöhte Laufzeit problematisch. Zur Reduktion der fehlerhaften Detektionen werden zwei neue Ansätze vorgeschlagen. Beide Ansätze verfolgen dabei das Ziel, die verwendete Merkmalsrepräsentation durch zusätzliche Kontextinformationen zu verbessern. Der erste Ansatz verfeinert die räumlichen Kontextinformationen durch eine Kombination der Merkmale von frühen und tiefen Schichten der zugrundeliegenden CNN Architektur, so dass feine und grobe Strukturen besser repräsentiert werden. Der zweite Ansatz macht Gebrauch von semantischer Segmentierung um den semantischen Informationsgehalt zu erhöhen. Hierzu werden zwei verschiedene Varianten zur Integration der semantischen Segmentierung in das Detektionsverfahren realisiert: zum einen die Verwendung der semantischen Segmentierungsergebnisse zur Filterung von unwahrscheinlichen Detektionen und zum anderen explizit durch Verschmelzung der CNN Architekturen zur Detektion und Segmentierung. Sowohl durch die Verfeinerung der räumlichen Kontextinformationen als auch durch die Integration der semantischen Kontextinformationen wird die Anzahl der fehlerhaften Detektionen deutlich reduziert und somit die Detektionsgenauigkeit erhöht. Insbesondere der starke Rückgang von fehlerhaften Detektionen in unwahrscheinlichen Bildregionen, wie zum Beispiel auf Gebäuden, zeigt die erhöhte Robustheit der gelernten Merkmalsrepräsentationen. Zur Reduktion der Laufzeit werden im Rahmen der Arbeit zwei alternative Strategien verfolgt. Die erste Strategie ist das Ersetzen der zur Merkmalsextraktion standardmäßig verwendeten CNN Architektur mit einer laufzeitoptimierten CNN Architektur unter Berücksichtigung der Eigenschaften der Luftbilddaten, während die zweite Strategie ein neues Modul zur Reduktion des Suchraumes umfasst. Mit Hilfe der vorgeschlagenen Strategien wird die Gesamtlaufzeit sowie die Laufzeit für jede Komponente des Detektionsverfahrens deutlich reduziert. Durch Kombination der vorgeschlagenen Ansätze kann sowohl die Detektionsgenauigkeit als auch die Laufzeit im Vergleich zur Faster R-CNN Baseline signifikant verbessert werden. Repräsentative Ansätze zur Fahrzeugdetektion in Luftbilddaten aus der Literatur werden quantitativ und qualitativ auf verschiedenen Datensätzen übertroffen. Des Weiteren wird die Generalisierbarkeit des entworfenen Ansatzes auf ungesehenen Bildern von weiteren Luftbilddatensätzen mit abweichenden Eigenschaften demonstriert

Deep Learning based Vehicle Detection in Aerial Imagery

This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced

Deep Learning Methods for 3D Aerial and Satellite Data

Recent advances in digital electronics have led to an overabundance of observations from electro-optical (EO) imaging sensors spanning high spatial, spectral and temporal resolution. This unprecedented volume, variety, and velocity is overwhelming our capacity to manage and translate that data into actionable information. Although decades of image processing research have taken the human out of the loop for many important tasks, the human analyst is still an irreplaceable link in the image exploitation chain, especially for more complex tasks requiring contextual understanding, memory, discernment, and learning. If knowledge discovery is to keep pace with the growing availability of data, new processing paradigms are needed in order to automate the analysis of earth observation imagery and ease the burden of manual interpretation. To address this gap, this dissertation advances fundamental and applied research in deep learning for aerial and satellite imagery. We show how deep learning---a computational model inspired by the human brain---can be used for (1) tracking, (2) classifying, and (3) modeling from a variety of data sources including full-motion video (FMV), Light Detection and Ranging (LiDAR), and stereo photogrammetry. First we assess the ability of a bio-inspired tracking method to track small targets using aerial videos. The tracker uses three kinds of saliency maps: appearance, location, and motion. Our approach achieves the best overall performance, including being the only method capable of handling long-term occlusions. Second, we evaluate the classification accuracy of a multi-scale fully convolutional network to label individual points in LiDAR data. Our method uses only the 3D-coordinates and corresponding low-dimensional spectral features for each point. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6\%. Finally, we validate the prediction capability of our neighborhood-aware network to model the bare-earth surface of LiDAR and stereo photogrammetry point clouds. The network bypasses traditionally-used ground classifications and seamlessly integrate neighborhood features with point-wise and global features to predict a per point Digital Terrain Model (DTM). We compare our results with two widely used softwares for DTM extraction, ENVI and LAStools. Together, these efforts have the potential to alleviate the manual burden associated with some of the most challenging and time-consuming geospatial processing tasks, with implications for improving our response to issues of global security, emergency management, and disaster response

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}

PERCEPTION FOR SURVEILLANCE: LEARNING SELF-LOCALISATION AND INTRUDERS DETECTION FROM MONOCULAR IMAGES OF AN AERIAL ROBOT IN OUTDOOR URBAN ENVIRONMENTS

Unmanned aerial vehicles (UAVs), more commonly named drones, are one of the most versatile robotic platforms for their high mobility and low-cost design. Therefore, they have been applied to numerous civil applications. These robots generally can complete autonomous or semi-autonomous missions by undertaking complex calculations on their autopilot system based on the sensors' observations to control their attitude and speed and to plan and track a trajectory for navigating in a possibly unknown environment without human intervention. However, to enable higher degrees of autonomy, the perception system is paramount for extracting valuable knowledge that allows interaction with the external world. Therefore, this thesis aims to solve the core perception challenges of an autonomous surveillance application carried out by an aerial robot in an outdoor urban environment. We address a simplified use case of patrolling missions to monitor a confined area around buildings that is supposedly under access restriction. Hence, we identify the main research questions involved in this application context. On the one hand, the drone has to locate itself in a controlled navigation environment, keep track of its pose while flying, and understand the geometrical structure of the 3D scene around it. On the other hand, the surveillance mission entails detecting and localising people in the monitored area. Consequently, we develop numerous methodologies to address these challenging questions. Furthermore, constraining the UAV's sensor array to a monocular RGB camera, we approach the raised problems with algorithms in the computer vision field. First, we train a neural network with an unsupervised learning paradigm to predict the drone ego-motion and the geometrical scene structure. Hence, we introduce a novel algorithm that integrates a model-free epipolar method to adjust online the rotational drift of the trajectory estimated by the trained pose network. Second, we employ an efficient Convolutional Neural Network (CNN) architecture to regress the UAV global metric pose directly from a single colour image. Moreover, we investigate how dynamic objects in the camera field of view affect the localisation performance of such an approach. Following, we discuss the implementation of an object detection network and derive the equations to find the 3D position of the detected people in a reconstructed environment. Next, we describe the theory behind structure-from-motion and use it to recreate a 3D model of a dataset recorded with a drone at the University of Luxembourg's Belval campus. Ultimately, we perform multiple experiments to validate and evaluate our proposed algorithms with other state-of-the-art methodologies. Results show the superiority of our methods in different metrics. Also, in our analysis, we determine the limitations and highlight the benefits of the adopted strategies compared to other approaches. Finally, the introduced dataset provides an additional tool for benchmarking perception algorithms and future application developments