Accuracy assessment of land use classification using hybrid methods

Chang, Kuan-Tsung; Yiu, F. G.; Hwang, Jin-Tsong; Lin, Yu‐Hsin

doi:10.1117/12.976844

Cited by 4 publications

(1 citation statement)

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“…The advantages of employing remote sensing technology to examine LULC include extensive range, immediacy and periodicity of the obtainable information. Remote sensing technology can be applied to monitor current changes in LULC (Chang et al 2012b).…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification Accuracy Assessment Using Full-Waveform LiDAR Data

Chang¹,

Feng²,

Chang³

et al. 2015

Terr. Atmos. Ocean. Sci.

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The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.

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Section: Introductionmentioning

confidence: 99%