Hans Koren

Tools for interactive map conversion and vectorization

The process of converting an analog map into structured digitized information requires several different operations, which are all time-consuming when performed manually. Strictly automatic processing is not always a possible solution, and an interactive approach can then be an alternative. The paper describes a tool for map conversion, focusing on the functionality for extraction of line structures. An interactive approach is used as it gives the user an opportunity to survey the process, and utilize human knowledge. The methods are based on contour following, extracting centre points needed for accurate vector representation of the line during tracing.

Traffic Monitoring Using Very High Resolution Satellite Imagery

Very high resolution satellite images allow automated monitoring of road traffic conditions. Satellite surveillance has several obvious advantages over current methods, which consist of expensive single-point measurements made from pressure sensors, video surveillance, etc., in/or close to the road. The main limitation of using satellite surveillance is the time resolution; the continuously changing traffic situation must be deduced from a snapshot image. In cooperation with the Norwegian Road Authorities, we have developed an approach for detection of vehicles in Quick-Bird images. The algorithm consists of a segmentation step followed by object-based maximum likelihood classification. Additionally, we propose a new approach for prediction of vehicle shadows. The shadow information is used as a contextual feature in order to improve classification. The correct classification rate was 89 percent, excluding noise samples. The proposed method tends to underestimate the number of vehicles when compared to manual counts and in-road equipment counts.

Multi-sensor monitoring of snow parameters in Nordic mountainous areas

Hydropower users require timely updated information about snow coverage and snow melting during the important snow-melting period in Nordic mountains. In this paper we report results from a series of experiments performed to map snow parameters with optical and radar remote sensing. A near real-time pre-operational system has been developed to provide timely snow cover mapping over Nordic mountainous areas for hydropower users. The multi sensor and multi temporal snow cover maps are based on single sensor snow maps from SAR and optical sensors. Each data acquisition over the area are classified into snow maps and projected on a common grid. A confidence raster in also produced where the accuracy of the classification of each pixel in the snow map is represented as a confidence value between 0 and 100% depending on incidence angle, probability of clouds and wet/dry. Each single sensor product is fused to the latest multisensor product with its associated confidence image to produce an updated snow map. The sensors used in the demonstration of the preoperational multi sensor snow mapping system are Envisat ASAR and Terra Modis. Testing has been done in 2003 and 2004 and continues in the melting season of 2005.

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.

A new global Snow Extent product based on ATSR-2 and AATSR

The ESA project GlobSnow develops products and services for snow extent and snow water equivalent. The time series of Snow Extent (SE) products will cover the whole seasonally snow-covered Earth for the years 1995–2010 based on the optical sensors ERS-2 ATSR-2 and Envisat AATSR data. A laboratory processing chain has been developed for testing and improving algorithms in an iterative process. The final version of the laboratory processing chain will function as a reference system for the implementation of an operational system for production of the full time series of products as well as near-real-time products produced on a daily basis. The first version of the SE product set spanning 15 years of the Northern Hemisphere is expected to be ready by the end of 2010 and will be made freely available.

The suitability of future high-resolution satellite imagery for forest inventory

Within a year or two, the first non-military high-resolution imaging satellites will be in orbit. Data from such satellites looks very promising for forest mapping in heterogeneous forest with small stands, like the Norwegian forests. The experiment presented in this article has tested how suitable such data may be for forest mapping. Aerial imagery has been digitized and converted to panchromatic and three-band multispectral data. Such data sets have been generated for five configurations of panchromatic and multispectral resolutions. Spectral and textural features have been extracted from each data set and classified into a selected subset of classes of interest in forestry. The results show that high-resolution data increases the performance of the classification significantly. There was a 16% reduction in the total error rate when the resolution was increased from 15 m panchromatic/30 m multispectral to 1 m panchromatic/4 m multispectral.

An Approach for Multisensor Harmonization in Snow Cover Area Mapping

In this study, we have developed an approach for fusion of optical and SAR data for snow cover fraction (SCF) retrieval that avoids the typical blending effects when combining independently retrieved geophysical data from different sensors. Instead of undertaking the sensor fusion at the geophysical parameter level, the fusion is done at the electromagnetic signal level. A state model, based on hidden Markov model theory, has been developed for the simultaneous signal from the optical and the SAR sensors. The model goes through a given set of states through the snowmelt season where transition probability distribution functions of time have been determined for each state transition. A coupling between corresponding models for optical and SAR observations has been developed in order to make a more reliable model of the sensor co-variation.