Sidharta Gautama

TL;DR: 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.

TL;DR: 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, resulting in the results obtained by the winners of both tracks.

TL;DR: In this letter, a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI and MPs) and MPs (built on both HS and LiDAR data) is proposed, where the edges of the fusion graph are weighted by the distance between the stacked feature points.

TL;DR: This paper introduces two improvements on the use of morphological profiles by using linear SEs and shows that the addition of the line-based MP gives a substantial improvement of the classification result.

TL;DR: A comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods, and presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensingData.