Sidharta Gautama

Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.

Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based MP gives a substantial improvement of the classification result. A second improvement is achieved by using ldquopartial morphological reconstructionrdquo instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to ldquoover-reconstructionrdquo in typical remote sensing images and a decreased classification performance. With ldquopartial reconstruction,rdquo we are able to overcome this problem and still preserve the shape of objects.

Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.

Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.

Evaluation of stereo matching algorithms for occupant detection

Vision systems within vehicles offer new opportunities in the automobile industry. The detection and classification of passenger and driver seat occupancy open up new ways to improve the safety and comfort of the passengers. We present results of a stereo system designed for the observation of the cockpit scene in order to provide information about the passenger presence and location within the vehicle to improve the control of the airbag firing. We compare different techniques and examine the effect of random and systematic errors on the performance in precision, robustness and processing speed. These results establish a foundation for on-going work on occupant detection for vehicle safety.

Evaluating corner detectors for the extraction of man-made structures in urban areas

We analyze if the presence of corners in very high resolution (VHR) satellite images can give us an indication on the type of structure present in a scene (man-made versus natural structures). Two the corner detectors are validated in this respect: Harris and SUSAN. The detection performance is evaluated over a spectrum of spatial resolutions for current and future VHR systems (from 2 meters to 17 centimeters). The ground truth of this study consists of annotated image extracts containing different types of man-made structures, in which the relevant corners have been identified.

Smart city mobility application-gradient boosting trees for mobility prediction and analysis based on crowdsourced data

Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process).

Policy 2.0 platform for mobile sensing and incentivized targeted shifts in mobility behavior

Sustainable mobility and smart mobility management play important roles in achieving smart cities’ goals. In this context we investigate the role of smartphones as mobility behavior sensors and evaluate the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones. The empirical results are based on mobile sensed data collected from more than 3400 people’s real life over a period of six months. The findings show which user profiles are most likely to accept such incentives and how likely they are to result in more sustainable mode choices. In addition we provide insights into tendencies towards accepting more sustainable route options for different trip purposes and illustrate smart city platform potential (for collection of mobility behavior data and delivery of incentives) as a tool for development of personalized mobility management campaigns and policies.

The use of smartphone applications in the collection of travel behaviour data

The MOVE project deals with the collection and analysis of crowd behaviour data. The main goals of the project are to collect data through the use of mobile phones and to develop new technologies to process and mine the collected data for crowd behaviour analysis. This paper describes the different steps in the development of tracking applications for smartphones that make use of advanced data mining. The results on data collection, analysis, and reporting have led to the development and operation of an advanced urban data monitoring system.

Fully automatic and robust UAV camera calibration using chessboard patterns

Due to weight constraints, UAVs often carry cameras with lenses that create distortions in the image. For practical applications this distortion should be removed with a proper calibration procedure, without spending too much extra time. Existing methods require costly manual interaction when the grid is not fully visible, or when not all points can be extracted. In this paper we present an algorithm to perform the calibration without any user interaction whatsoever, which works under almost all possible conditions. The only inputs are a number of pictures of a checkerboard, taken with the camera. We extract the corners from the chessboard pictures, and set up a world coordinate grid that is robust to missing corner points, occlusion and deformations. We automatically omit the pictures that are too close to another picture, to avoid giving too much weight to often viewed areas. Finally we optimize the result by iteratively removing outlier pictures from the set.