Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Semantic segmentation of roof materials in urban environment by utilizing hyperspectral and LiDAR data

Mapping areas in an urban environment can be challenging due to various materials and manufactured structures. The urban environment is a mix of natural and artificial materials, and finding the right object of a specific material is a challenge even for the trained eye. Therefore, by applying high spectral resolution hyperspectral imagery it is possible to examine surface materials based on spectral signature. Combined with LiDAR, it is also feasible to detect the geometrical structure of the surface. These data can be exposed to a machine learning algorithm to recognize objects automatically. In this study machine learning algorithms are exposed to airborne images of roof materials. This thesis presents an application of semantic segmentation for roof materials based on fused hyperspectral (HySpex VNIR-1800 and SWIR-384) and LiDAR (Riegl VQ-560i) data acquired from 2021 over Bærum municipality near Oslo in Norway. The machine learning algorithm is a semantic segmentation model named Res-U-net with a U-net architecture and a ResNet34 backbone. The Res-U-Net is a supervised neural network with high capacity to learn high-dimensional airborne data. The model returns a mask of the urban area that pinpoints the roofs’ position and materials. The ground truth is generated with information from field work, a geographical database and the watershed algorithm for object detection. This ground truth consists of nine different roof materials and background. The semantic segmentation model is optimized by testing different model configurations for this specific problem. The best model scores 0.903, 0.896, and 0.579 in accuracy score, F1 score weighted and Matthews Correlation Coefficient. For the binary problem of detecting roof the model scores 0.948, 0.946, and 0.767 on the same metrics. This study demonstrates that semantic segmentation is viable for localizing and classifying roof materials with fused hyperspectral and LiDAR data. Such an analysis can potentially automate several mapping chores and manual assignments by systemically processing a larger area in a short time to free human capacity.M-M

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports

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

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)