Regions of Interest Detection in Panchromatic Remote Sensing Images Based on Multiscale Feature Fusion
Regions of Interest Detection in Panchromatic Remote Sensing Images Based on Multiscale Feature Fusion
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DOI：
10.1109/jstars.2014.2319736
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发表时间：
2014-05
影响因子：
5.5
通讯作者：
Li-bao Zhang;Kaina Yang;Hao Li
中科院分区：
文献类型：
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作者：
Li-bao Zhang;Kaina Yang;Hao Li
传统的基于先验知识的感兴趣区域（roi）检测方法在处理高分辨率遥感图像时，通常采用全局搜索解决方案，导致计算过于复杂。为了解决这一问题，本研究提出了一种基于多尺度特征融合的更快、更高效的ROI检测算法，该算法将输入图像沿强度和方向两个特征通道进行处理。提出了计算强度显著性的多尺度谱残差法。采用插值双正交整数小波变换（IB-IWT）提取方向特征，通过阈值分割和滤波获得方向显著性。提出了一种加权跨比例尺融合方法，将不同比例尺的显著性图合并为一张图，同时保留不同比例尺的显著性区域。实验结果表明，新算法计算效率高，视觉检测结果更准确。
A global searching solution was often employed in traditional prior-knowledge-based regions of interest (ROIs) detection methods for processing high-resolution remote sensing images, which results in prohibitively complex computing. To solve this problem, this study proposes a faster and more efficient ROI detection algorithm based on multiscale feature fusion, wherein the input image is processed along two feature channels: intensity and orientation. The multiscale spectrum residuals method is proposed to compute intensity saliency. The interpolating biorthogonal integer wavelet transform (IB-IWT) is used to extract orientation features, and the orientation saliency is obtained with thresholding and filtering. A weighted across-scale fusion method is proposed to combine conspicuity maps at different scales into one map while retaining salient regions at different scales. The experimental results reveal that the new algorithm is computationally efficient and provides more visually accurate detection results.