Rich probabilistic models for semantic labeling

Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure

Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion

Semantic segmentation from aerial views is a crucial task for autonomous drones, as they rely on precise and accurate segmentation to navigate safely and efficiently. However, aerial images present unique challenges such as diverse viewpoints, extreme scale variations, and high scene complexity. In this paper, we propose an end-to-end multi-class semantic segmentation diffusion model that addresses these challenges. We introduce recursive denoising to allow information to propagate through the denoising process, as well as a hierarchical multi-scale approach that complements the diffusion process. Our method achieves competitive results on the UAVid dataset and state-of-the-art performance on the Vaihingen Building segmentation benchmark. Being the first iteration of this method, it shows great promise for future improvements

Spatiotemporal Fusion in Remote Sensing

Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Sea ice segmentation in SAR images using Deep Learning

Sea ice covers over seven percent of the world's oceans. Due to the effect of global warming, Arctic's ice extent has decreased significantly in the past decades. This reduction in sea ice cover is opening new pathways for the international shipping community through the Arctic. Due to the lengthening of the open water season, the Canadian Arctic has also observed a three-fold increase in the shipping traffic in the past few years. Although the ice extent has reduced, the risks and hazards involved in shipping through these regions are still significant. To promote safe and efficient maritime activities in the Canadian Arctic, Canadian Ice Service (CIS) provides information about ice in Canada's navigable waters. CIS uses Synthetic Aperture Radar (SAR) images as one of the prominent sources to gain insights about the ice conditions in Canadian waters. Automated SAR image interpretation is a complex task and requires algorithms to learn complex and rich features. Convolutional neural networks (CNNs) have demonstrated their ability to learn such features and have been used in various image classification, segmentation and object detection tasks. In this thesis, we first propose a method to detect marginal ice zones (MIZs) in SAR images. This method uses transfer learning combined with a multi-scale patch technique to detect the MIZs. The multi-scale patch technique involves generating the segmentation masks over different patch sizes for the same image. These masks are later stacked together and thresholded to generate the final MIZ prediction mask for an image. Later we dive deep into the MIZs and focus on segmenting sea ice floes. We propose a segmentation model optimized for the task of ice floe segmentation in SAR images. The model is based on a fully convolutional architecture with residual connections. In addition to this, a conditional random field is also used as a post-processing step. The whole network is trained end-to-end using a dual loss function. Qualitative and quantitative analysis suggests that our model beats the conventional segmentation architectures for the task of ice floe detection