GDAL ドライバー オプションと拡張されたファイル名から現メタデータを管理します。残留登録フレームワークはジオメトリ モデル化センサーの改善に基づいています。特徴抽出アプリケーション、分類、分類正則化とステレオ画像ペアから 1 つをクリックして DEM 生成の融合。
タグ:
stereo, Fusion, feature extraction, GDAL
Manages GDAL driver options and sesor metadata through extended filenames. The framework for residual registration is based on improvements in the sensor modeling geometry. Feature extraction applications, fusion of classifiers, classification regularization, and one-click DEM generation from a stereo image pair.
このリリースはプリアードの画像は、新しい大きなベクトル データセットを効率的に処理する GDAL/OGR のセグメンテーション、および、拡張スイートのフィルターと raw のステレオペアから実質カート グラフィックのデジタル標高モデルにすべての方法を行くに関連付けられているアプリケーションを実行する完全なフレームワークに基づくクラスのセットを完全にサポートを追加します。
This release adds full support for Pléiades imagery, a new set of classes based on GDAL/OGR to handle large vector datasets efficiently, a complete framework to perform segmentation, and an extended suite of filters and associated applications to go from raw stereo pairs all the way to a real cartographic Digital Elevation Model.
Large JPEG2000 file (Pleiades-like) support and Pleiades metadata handling. Efficient JPEG2000 visualization and ROI decompression tools in Monteverdi. A revamp of otb applications in a generic and scalable framework: launch applications from the command line, from an auto-generated QT GUI, from Python, from within QGis, etc. There are many new algorithms: Dimensionality Reduction (ICA, PCA, MNF, MAF, etc.), change detection (MAD), Hyperspectral Unmixing, elevation map from stereo data, compare segmentation with a ground truth (Hoover), etc. There are various bugfixes.
Lots of new features were added, including a multi-image supervised classification suite in OTB-Applications, an application to perform segmentation based on connected components and object based image analysis, a framework for cartographic database creation, and validation based on the fusion of features in the framework of Dempster-Shafer evidence theory.