書誌事項
- タイトル別名
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- Object-based forest type classification using texture and spectral features from high-resolution satellite images
- コウブンカイノウ エイセイ ガゾウ ノ テクスチャ トクチョウリョウ ト スペクトル トクチョウリョウ オ モチイタ オブジェクト シコウガタ リンブン タイプ ブンルイ
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This paper shows the object-based forest type classification using texture features from a panchromatic (PAN) image and spectral features from a multispectral (MS) image obtained by the QuickBird satellite.To investigate the performance of each feature, first, only texture feature is applied to image analysis, then spectral feature, and lastly combination of texture and spectral features.In this analysis, we use common segments obtained from a pansharpen image in order to compare the difference only between texture and spectral features.Distance between supervised classes is used to find well distinguishing feature combinations for classes.For PAN image analysis, 4 texture features from 8 candidates generated from co-occurrence matrix were selected. For MS image analysis, 9 spectral features from 10 candidates, such as 4 bands value and 6 differences between 2 bands from 4 bands, were selected.For PAN and MS analysis, 3 texture features from 8 candidates and all 10 spectral features were selected.Overall accuracy and Cohen's kappa of 6 forest types classification were 32.6% and 20.4% for PAN image, 74.6% and 70.6% for MS image, and 79.3% and 76.0% for PAN and MS images.This study demonstrated that combination of texture and spectral features exceeds a single feature in accuracy.
収録刊行物
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- 写真測量とリモートセンシング
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写真測量とリモートセンシング 46 (2), 27-36, 2007
一般社団法人 日本写真測量学会
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詳細情報 詳細情報について
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- CRID
- 1390282679054205696
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- NII論文ID
- 10024353560
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- NII書誌ID
- AN00111450
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- ISSN
- 18839061
- 02855844
- http://id.crossref.org/issn/02855844
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- NDL書誌ID
- 8819645
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可