Yi Liu

DNV - Det Norske Veritas · GRD - Group of Research and Development

Superpixels are a powerful device to characterize the spatial-contextual information in remotely sensed hyperspectral image (HSI) interpretation. However, the exploitation of superpixels in classification problems is not straightforward, often leading to unbearable NP-hard discrete integer optimization problems. In this paper, we attack this hurdle...

Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with...

The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish assemblages when estimating biomass/abundance. Here, an uns...

In this study, radio channel measurements were conducted in an urban inland river environment at 5.9 GHz. The measurements consisted of both the line of sight (LOS) and non-line of sight (NLOS) cases. We estimate and model channel characteristics based on the measured data. For small-scale fading properties, best fit small-scale fading distribution...

This study focused on differences in vehicle-to-vehicle radio channel characteristics in the same region but different traffic density and speeds at 5.9 GHz (congestion and non-congestion). The continuous measurement campaign was conducted on a city expressway through the complex dense urban area in Wuhan, China. Small-scale channel characteristics...

Abundance information has been recently used to assist hyperspectral image classification by combining the information coming from classification and unmixing. The fact that classes are usually inconsistent with endmembers makes it a crucial issue to find possible connections between classification and unmixing. This paper describes a new class-bas...

Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral im...

Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspect...

Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for hyperspectral image classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice, this signal may be obtain...

In this paper, we present a new approach for spectral partitioning which is intended to deal with ill-posed problems in hyperspectral image classification. First, we use adaptive affinity propagation (AAP) to intelligently group the original spectral bands. Such grouping strategy not only allows us to reduce the number of spectral bands, but also t...

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture o...