R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor

要約

マルチモーダル画像間の特徴的な対応関係を自動的に識別することは、放射線と幾何学的な差異が大きいため、非常に困難である。これらの問題を解決するために、我々は放射線と回転の違いに頑健なR2FD2と名付けた新しい特徴マッチング手法を提案する。我々のR2FD2は、繰り返し可能な特徴検出器と回転不変な特徴記述子からなる二つの重要な貢献によって行われる。第一段階では、特徴検出のために、多チャンネル自己相関とLog-Gaborウェーブレットを組み合わせた反復性特徴検出器を提示し、高い反復性と均一な分布で関心点を検出する。第2段階では，回転不変な特徴記述子，すなわちLog-Gaborの回転不変最大指標マップを構築する．利き手を高速に割り当てる過程では，回転変形に対応した回転不変最大指標マップを構築する．そして、提案するRMLGは、回転不変なRMIMとDAISYの空間配置を組み合わせて、より識別性の高い特徴表現を描き、RMLGの放射線や回転変動に対する耐性を向上させる。

要約(オリジナル)

Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method, named R2FD2, that is robust to radiation and rotation differences.Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor, which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLGs resistance to radiation and rotation variances.

arxiv情報

提供元, 利用サービス

arxiv.jp, DeepL