Using multiscale texture information from ALOS PALSAR to map tropical forest

DOI Web Site 参考文献22件
  • Preesan Rakwatin
    a Geo-Informatics and Space Technology Development Agency (Public Organization) , Lak Si , Bangkok , 10210 , Thailand
  • Nicolas Longépé
    b Collecte Localization Satellites (CLS) , Plouzane , 29280 , France
  • Osamu Isoguchi
    c Earth Observation Research Centre, Japan Aerospace Exploration Agency, Tsukuba Space Centre , Tsukuba , Ibaraki , 305-8505 , Japan
  • Masanobu Shimada
    c Earth Observation Research Centre, Japan Aerospace Exploration Agency, Tsukuba Space Centre , Tsukuba , Ibaraki , 305-8505 , Japan
  • Yumiko Uryu
    d World Wildlife Fund (WWF) , Washington , DC , 20037 , USA
  • Wataru Takeuchi
    e Institute of Industrial Science, The University of Tokyo , Meguro , Tokyo , 153-8505 , Japan

書誌事項

公開日
2012-07-06
資源種別
journal article
DOI
  • 10.1080/01431161.2012.701349
公開者
Informa UK Limited

この論文をさがす

説明

This research investigated the ability of the Advanced Land Observing Satellite ALOS Phased Array type L-band Synthetic Aperture Radar PALSAR to map tropical forest in central Sumatra, Indonesia. The study used PALSAR 50 m resolution orthorectified HH and HV data. As land-cover discrimination is difficult with only two bands HH and HV, we added textures as additional information for classification. We calculated both first-and second-order texture features and studied the effects of texture window size, quantization scale and displacement length on discrimination capability. We found that rescaling to a lower number of grey levels 8 or 16 improved discrimination capability and that equal probability quantization was more effective than uniform quantization. Increasing displacement tended to reduce the discrimination capability. Low spatial resolution increased the discrimination capability because low spatial resolution features reduce the effects of noise. A larger number of features also improved discrimination capability. However, the amount of improvement depended on the window size. We used the optimum combination of backscatter amplitude and textures as input data into a supervised multi-resolution maximum likelihood classification. We found that including texture information improved the overall classification accuracy by 10%. However, there was significant confusion between natural forest and acacia plantations, as well as between oil palm and clear cuts, presumably because the backscatter and texture of these class pairs are very similar.

収録刊行物

参考文献 (22)*注記

もっと見る

関連プロジェクト

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ