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Least square approach for subpixel target detection on multispectral remotely sensed imagery

机译：最小二乘方法在多光谱遥感影像上进行亚像素目标检测

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摘要

Least square unmixing approach has been successfully applied in hyperspectral image processing for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands. This paper presents an extension of the least square approach that relaxes this constraint with a set of least square filters that are nonlinearly combined for endmember detection. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP).
机译：最小二乘分解法已经成功地应用于高光谱图像处理中，用于亚像素目标检测。通过估计每个像素中存在的目标丰度，它可以检测尺寸小于像素的目标。为了使该方法有效，带的数量必须大于或等于要分类的签名的带数量，即等式的数量应不小于未知数的数量。这确保了有足够的尺寸来容纳由各个书帖产生的正交投影。这被称为带号约束（BNC）。对于高光谱图像，这样的固有约束不是问题，因为它们通常具有数百个频带，这大于驻留在图像中的签名的数量。但是，这对于要分类的签名数量大于波段数量的多光谱图像可能不是正确的。本文介绍了最小二乘方法的扩展，它使用一组非线性组合用于端成员检测的最小二乘滤波器来放宽此约束。通过SPOT图像评估了该方法的有效性。实验结果表明，与正交子空间投影（OSP）相比，分类性能显着提高。

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