Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point Clouds

This work presents a new depth- and semantics-aware conditional generative model, named TITAN-Next, for cross-domain image-to-image translation in a multi-modal setup between LiDAR and camera sensors. The proposed model leverages scene semantics as a mid-level representation and is able to translate raw LiDAR point clouds to RGB-D camera images by solely relying on semantic scene segments. We claim that this is the first framework of its kind and it has practical applications in autonomous vehicles such as providing a fail-safe mechanism and augmenting available data in the target image domain. The proposed model is evaluated on the large-scale and challenging Semantic-KITTI dataset, and experimental findings show that it considerably outperforms the original TITAN-Net and other strong baselines by 23.7$\%$ margin in terms of IoU

Advancing Land Cover Mapping in Remote Sensing with Deep Learning

Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework

Deep Learning for Building Footprint Generation from Optical Imagery

Auf Deep Learning basierende Methoden haben vielversprechende Ergebnisse für die Aufgabe der Erstellung von Gebäudegrundrissen gezeigt, aber sie haben zwei inhärente Einschränkungen. Erstens zeigen die extrahierten Gebäude verschwommene Gebäudegrenzen und Klecksformen. Zweitens sind für das Netzwerktraining massive Annotationen auf Pixelebene erforderlich. Diese Dissertation hat eine Reihe von Methoden entwickelt, um die oben genannten Probleme anzugehen. Darüber hinaus werden die entwickelten Methoden in praktische Anwendungen umgesetzt

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Using encoder-decoder architecture for material segmentation based on beam profile analysis

Abstract. Recognition and segmentation of materials has proven to be a challenging problem because of the wide divergence in appearance within and between categories. Many recent material segmentation approaches treat materials as yet another set of labels like objects. However, materials are basically different from objects as they have no basic shape or defined spatial extent. Our approach roughly ignores this and can primarily take advantage of limited implicit context (local appearance) as it seems during training, because our training images that almost do not have a global image context; such as (I) where the used materials have no inherent shape or defined spatial extent like apple, orange and potato approximately have the same spherical shape; (II) besides, images where taken under a black background, which roughly removes the spatial features of the materials. We introduce a new materials segmentation dataset, which was taken with a Beam Profile Analysis sensing device. The dataset contains 10 material categories, and it has image pair samples consisting of grayscale images with and without the laser spots (grayscale and laser images) in addition to annotated segmented images. To the best of our knowledge, this is the first material segmentation dataset for Beam Profile Analysis images. As a second step, we proposed a deep learning approach to perform material segmentation on our dataset; our proposed CNNs is an encoder-decoder model, which is based on the DeeplabV3+ model. Our main goal is to obtain segmented material maps and discover how the laser spots contribute to the segmentation results; therefore, we perform a comparative analysis across different types of architectures to observe how the laser spots contribute to the whole segmentation. We built our experiments on three main types of models that use a different type of input; for each model, we implemented various types of backbone architectures. Our experiments results show that the laser spots have an efficient contribution on the segmentation results. GrayLaser model achieves a significant accuracy improvement compared to other models, where the fine-tuned architecture of this model has reached an accuracy of 94% over MIoU metric, and one trained from the scratch has reached an accuracy of 62% over MIoU

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Multi-task near-field perception for autonomous driving using surround-view fisheye cameras

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0 - 10 Metern und 360° Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a biological perception algorithm capable of driving cars, operating machinery, piloting aircraft, and navigating ships over millions of years. Automating these capabilities for computers is critical for various applications, including self-driving cars, augmented reality, and architectural surveying. Near-field visual perception in the context of self-driving cars can perceive the environment in a range of 0 - 10 meters and 360° coverage around the vehicle. It is a critical decision-making component in the development of safer automated driving. Recent advances in computer vision and deep learning, in conjunction with high-quality sensors such as cameras and LiDARs, have fueled mature visual perception solutions. Until now, far-field perception has been the primary focus. Another significant issue is the limited processing power available for developing real-time applications. Because of this bottleneck, there is frequently a trade-off between performance and run-time efficiency. We concentrate on the following issues in order to address them: 1) Developing near-field perception algorithms with high performance and low computational complexity for various visual perception tasks such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing optimization strategies that balance tasks