Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Chu, Hone-Jay; Wang, Chi-Kuei; Kong, Shish-Jeng; Chen, Kuei-Chia
• Is Part Of: GIScience & remote sensing. Volume 53:Issue 4(2016)
• Page Start: 542
• Page End: 559
• Publication Date: 2016-07-03
• Subjects: land-cover classification -- waveform LiDAR -- hyperspectral image -- data fusion -- principal component analysis -- minimum noise fraction; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1177249
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Hyperspectral image and full-waveform light detection and ranging (LiDAR) data provide useful spectral and geometric information for classifying land cover. Hyperspectral images contain a large number of bands, thus providing land-cover discrimination. Waveform LiDAR systems record the entire time-varying intensity of a return signal and supply detailed information on geometric distribution of land cover. This study developed an efficient multi-sensor data fusion approach that integrates hyperspectral data and full-waveform LiDAR information on the basis of minimum noise fraction and principal component analysis. Then, support vector machine was used to classify land cover in mountainous areas. Results showed that using multi-sensor fused data achieved better accuracy than using a hyperspectral image alone, with overall accuracy increasing from 83% to 91% using population error matrices, for the test site. The classification accuracies of forest and tea farms exhibited significant improvement when fused data were used. For example, classification results were more complete and compact in tea farms based on fused data. Fused data considered spectral and geometric land-cover information, and increased the discriminability of vegetation classes that provided similar spectral signatures.