To solve the problem of metamerism in the color reproduction process, various spectral reflectance reconstruction methods combined with neural network have been proposed in recent years. However, these methods are generally sensitive to initial values and can easily converge to local optimal solutions, especially on small data sets. In this paper, we propose a spectral reflectance reconstruction algorithm based on the Back Propagation Neural Network (BPNN) and an improved Sparrow Search Algorithm (SSA). In this algorithm, to solve the problem that BPNN is sensitive to initial values, we propose to use SSA to initialize BPNN, and we use the sine chaotic mapping to further improve the stability of the algorithm. In the experiment, we tested the proposed algorithm on the X-Rite ColorChecker Classic Mini Chart which contains 24 colors, the results show that the proposed algorithm has significantly better performance compared to other algorithms and moreover it can meet the needs of spectral reflectance reconstruction on small data sets. Code is avaible at https://github.com/LuraZhang/spectral-reflectance-reconsctuction.

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.