In hyperspectral sparse unmixing, a successful approach employs spectral
bundles to address the variability of the endmembers in the spatial domain.
However, the regularization penalties usually employed aggregate substantial
computational complexity, and the solutions are very noise-sensitive. We
generalize a multiscale spatial regularization approach to solve the unmixing
problem by incorporating group sparsity-inducing mixed norms. Then, we propose
a noise-robust method that can take advantage of the bundle structure to deal
with endmember variability while ensuring inter- and intra-class sparsity in
abundance estimation with reasonable computational cost. We also present a
general heuristic to select the \emph{most representative} abundance estimation
over multiple runs of the unmixing process, yielding a solution that is robust
and highly reproducible. Experiments illustrate the robustness and consistency
of the results when compared to related methods.

通过将多尺度空间正则化方法推广到无混合问题中，通过融合组稀疏诱导混合范数，提出了一种噪声鲁棒的方法，能够在合理的计算成本下利用束结构处理端元变异，并确保丰度估计的类内和类间稀疏性。同时，我们还提出了一种通用的启发式方法，通过多次运行混合过程选择得到最具代表性的丰度估计，从而获得具有鲁棒性和高可复现性的解决方案。实验结果表明，在与相关方法比较时，所得结果的鲁棒性和一致性。

广义多尺度捆绑式高光谱稀疏解混算法

A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing  Algorithm

Camera traps enable the automatic collection of large quantities of image
data. Ecologists use camera traps to monitor animal populations all over the
world. In order to estimate the abundance of a species from camera trap data,
ecologists need to know not just which species were seen, but also how many
individuals of each species were seen. Object detection techniques can be used
to find the number of individuals in each image. However, since camera traps
collect images in motion-triggered bursts, simply adding up the number of
detections over all frames is likely to lead to an incorrect estimate.
Overcoming these obstacles may require incorporating spatio-temporal reasoning
or individual re-identification in addition to traditional species detection
and classification.
We have prepared a challenge where the training data and test data are from
different cameras spread across the globe. The set of species seen in each
camera overlap, but are not identical. The challenge is to classify species and
count individual animals across sequences in the test cameras.

本文阐述了野生动物种群监测中的探照灯自动采集引进了大量图像数据，使用目标检测技术识别和计算每个物种个体数时需要考虑时空因素以及个体重新识别，同时描述了一个跨地区的物种分类和数量计算的挑战。