Ronny Hänsch

German Aerospace Center (DLR) | DLR · Microwaves and Radar Institute

Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) data covers a wide range of applications. SAR ATR helps to detect and track vehicles and other objects, e.g. in disaster relief and surveillance operations. Aircraft classification covers a significant part of this research area, which differs from other SAR-based ATR tasks, suc...

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many metho...

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diver...

This article presents the scientific outcomes of the 2023 Data Fusion Contest (DFC23) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest consists of two tracks investigating the fusion of optical and synthetic aperture radar data for 1) fine-grained roof type classifica...

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many metho...

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Carefully curated and annotated datasets are the foundation of machine learning (ML), with particularly data-hungry deep neural networks forming the core of what is often called artificial intelligence ( AI ). Due to the massive success of deep learning (DL) applied to Earth observation (EO) problems, the focus of the community has been largely...

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and econo...

Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on the fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of...

Datasets play a key role in developing superior building detection approaches. However, most of the previous work focuses on accurate building masks and scale expansion, while the categories are always missing, which hinders the further analysis of urban development and cultures. Therefore, we propose a benchmark for building detection and fine-gra...

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth Observation (EO) da...

In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, each designed for a specific data type and with a specific task or application in mind. In the jungle of remote sensing datasets, it can be hard to keep track of what is ava...

Object recognition in synthetic aperture radar images is a well studied topic that has gained a significant amount of attention within the last decades. Modern approaches are based on machine learning, i.e. deep learning, and often show excellent performance. What is so far missing in the literature is a study dedicated to the generalization capabi...

Floods are one of the major types of natural disasters responsible for loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Climate change, increasing populations, and urbanisation of flood plains will only increase the risk of flooding in the next few years. SpaceNet 8 prese...

Recently, the German TanDEM-X mission provided valuable acquisitions for developing models for the single-pass single-pol X-band forest height inversion. In this paper, the assessment of the two machine learning approaches to estimate forest height from the interferometric coherence are investigated and compared to the state-of-art physical models...

Pre-training deep neural networks uses a proxy task to learn representative features that are transferable to a different problem. We investigate how semantic segmentation networks for PolSAR images benefit from pre-training on a transcoding task which translates PolSAR data into optical images. We compare multiple approaches ranging from a simple...

In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, each designed for a specific data type and with a specific task or application in mind. In the jungle of remote sensing datasets, it can be hard to keep track of what is ava...

The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, a...

Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segmentation of polarimetric synthetic aperture radar images. By using internal projections that are defi...

The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, a...

We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and change...

Permafrost in the Northern hemisphere is rapidly warming in the context of climate change. The degradations associated to this trend pose several threats, locally to landscapes, infrastructures and settlements, and globally as permafrost is a potential source of greenhouse gazes in the carbon cycle. Different remote sensing methods can be used to m...

The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the living planet challenges. This paper reviews current deep learning approaches for Earth obser...

The Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aim...

Random Ferns - as a less known example of Ensemble Learning - have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segmentation of polarimetric synthetic aperture radar images. By using internal projections that are define...

In this paper, we elaborate on the scientific outcomes of the 2021 Data Fusion Contest (DFC2021), which was organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, on the subject of geospatial artificial intelligence (AI) for social good. The ultimate objective of the contest was to mod...

The creation of geospatial databases of large power infrastructure such as substations is essential for the planning and management of electricity transmission and distribution. Achieving this task through conventional mapping techniques involves great effort in terms of time, manpower and financial resources. Automatically extracting power infrast...

Presents information on the GRSS echnical Committee on Image Analysis and Data Fusion.

In this paper, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically co-sponsored by the IE...

Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation of remote sensing data. In this paper, we review the historic development of such datasets, discuss their features based on a few selected examples, and address open issu...

This paper summarizes study results obtained about the characteristics of sea ice in the Davis Strait off the coast of Baffin Island in 2019. The study, also referred to as ICESAR 2019, is based on multi-frequency and interferometric data collected by the DLR F-SAR airborne radar in the course of the PermASAR campaign in the Canadian Arctic with th...

Context - i.e. information not contained in a particular measurement but in its spatial proximity - plays a vital role in the analysis of images in general and in the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) images in particular. Nevertheless, a detailed study on whether context should be incorporated implicitly (e.g....

This paper presents the scientific outcomes of the 2020 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2020 Contest addressed the problem of automatic global land-cover mapping with weak supervision, i.e. estimating high-resolution semantic maps while on...

Reports on the GRSS 2021 Data Fusion Contest.

The Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has organized the annual Data Fusion Contest (DFC) since 2006. The contest aims to establish new benchmarks for scientific challenges in remote sensing image analysis by promoting the use of multimodal data, leveraging new senso...

We present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest included challenges with large-scale data sets for semantic 3D reconstruction from satellite images and also semantic 3D point cloud classification from...

In this paper, we present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2019 Contest addressed the problem of 3D reconstruction and 3D semantic understanding on a large scale. Several competitions were organized to as...

In the last years, vision-based systems have flourished at an unprecedented pace, fuelled by developments in hardware components (higher resolution and higher sensitivity imaging sensors, smaller and smarter micro controllers, just to name a few), as well as in software or processing techniques, with AI (Artificial Intelligence) leading to a landma...

The estimation of soil permittivity under fully covered grassland is a challenging task that can be approached by either model-based polarimetric decomposition techniques or data-driven machine-learning (ML) methods. In this study, we test the benefits and limitations of those techniques when individually or jointly applied to estimate the permitti...

Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. optical data is often the preferred choi...

Keypoints that do not meet the needs of a given application are a very common accuracy and efficiency bottleneck in many computer vision tasks, including keypoint matching and 3D reconstruction. Many computer vision and machine learning methods have dealt with this issue, trying to improve keypoint detection or the matching process. We introduce an...

Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. optical data is often the preferred choi...

Presents information on the 2020 IEEE GRSS Data Fusion Contest.

With the increasing importance of monitoring urban areas, the question arises which sensors are best suited to solve the corresponding challenges. This letter proposes novel node tests within the random forest (RF) framework, which allows them to apply them to optical RGB images, hyperspectral images, and light detection and ranging (LiDAR) data, e...

In the last years, vision-based systems have flourished at an unprecedented pace, fuelled by developments in hardware components (higher resolution and higher sensitivity imaging sensors, smaller and smarter micro controllers, just to name a few), as well as in software or processing techniques, with AI (Artificial Intelligence) leading to a landma...

The five papers in this special section focus on computer vision-based approaches for Earth observation. These papers followed a series of events promoting works at the interface between computer vision and remote sensing: the special sessions organized at the Living Planet Symposium1 and the Computer Vision and Pattern Recognition (CVPR) conferenc...

Presents information on the 2019 IEEE GRSS Data Fusion Contest.

The deployment of numerous air- and space-borne remote sensing sensors as well as new data policies led to a tremendous increase of available data. While methods such as neural networks are trained by online or batch processing, i.e. keeping only parts of the data in the memory, other methods such as Random Forests require offline processing, i.e....

The extraction of heart rate and other vital parameters from video recordings of a person has attracted much attention over the last years. In this paper, we examine time differences between distinct spatial regions using remote photoplethysmography (rPPG) in order to extract the blood flow path through human skin tissue in the neck and face. We ca...

The task of image colorization, i.e. assigninging color values to grayscale images, is usually addressed by either exploiting explicit user input or very large training data sets. In contrast, the proposed method is fully automatic and uses several orders of magnitude less training images. To this aim, a Random Forest is tailored to the task of reg...

This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2018 Contest addressed the problem of urban observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspe...

Presents information on the 2019 Data Fusion Contest.

Reports on the Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADFTC) of the IEEE Geoscience and Remote Sensing Society.

In this paper, we provide the first in-depth evaluation of exploiting Tomographic Synthetic Aperture Radar (TomoSAR) for the task of supervised land-cover classification. Our main contribution is the design of specific TomoSAR features to reach this objective. In particular, we show that classification based on TomoSAR significantly outperforms Pol...

Learning the proxy task of transcoding SAR images into optical images forces an employed conditional generative adversarial network (GAN) to distinguish between different land surfaces. Such a network can then be used to build a classifier with significantly fewer free parameters that generalizes well even when trained on a very small amount of lab...

The potential to positively influence research developments in seemingly unrelated areas leads to an increasing interest in the analysis of video games. As game publishers rarely provide an open interface to gain access to in-game information, the proposed system relies on the availability of video game recordings and broadcasts and operates comple...

Random Forests have continuously proven to be one of the most accurate, robust, as well as efficient methods for the supervised classification of images in general and polarimetric synthetic aperture radar data in particular. While the majority of previous work focus on improving classification accuracy, we aim for accelerating the training of the...