The dissection of hyperspectral images into intrinsic components through
hyperspectral intrinsic image decomposition (HIID) enhances the
interpretability of hyperspectral data, providing a foundation for more
accurate classification outcomes. However, the classification performance of
HIID is constrained by the model's representational ability. To address this
limitation, this study rethinks hyperspectral intrinsic image decomposition for
classification tasks by introducing deep feature embedding. The proposed
framework, HyperDID, incorporates the Environmental Feature Module (EFM) and
Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a
Feature Discrimination Module (FDM) is introduced to separate
environment-related and category-related features. Experimental results across
three commonly used datasets validate the effectiveness of HyperDID in
improving hyperspectral image classification performance. This novel approach
holds promise for advancing the capabilities of hyperspectral image analysis by
leveraging deep feature embedding principles. The implementation of the
proposed method could be accessed soon at this https URL
for the sake of reproducibility.

通过引入深度特征嵌入，提出了一种新颖的超光谱内在图像分解方法（HyperDID），该方法结合环境特征模块和分类特征模块来提取超光谱图像的内在特征，并引入特征判别模块来区分与环境和类别相关的特征，实验证实了 HyperDID 在改进超光谱图像分类性能方面的有效性。此方法的实施可以在此 URL 获得，便于复现。

HyperDID: 深度特征嵌入的高光谱内在图像分解

HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature  Embedding

We propose a novel camera-based DNN method for 3D lane detection with
uncertainty estimation. Our method is based on a semi-local, BEV, tile
representation that breaks down lanes into simple lane segments. It combines
learning a parametric model for the segments along with a deep feature
embedding that is then used to cluster segment together into full lanes. This
combination allows our method to generalize to complex lane topologies,
curvatures and surface geometries. Additionally, our method is the first to
output a learning based uncertainty estimation for the lane detection task. The
efficacy of our method is demonstrated in extensive experiments achieving
state-of-the-art results for camera-based 3D lane detection, while also showing
our ability to generalize to complex topologies, curvatures and road geometries
as well as to different cameras. We also demonstrate how our uncertainty
estimation aligns with the empirical error statistics indicating that it is
well calibrated and truly reflects the detection noise.

本文提出基于相机的 DNN 三维车道检测新方法，采用 BEV 矩形划分道路，结合深度特征嵌入建立车道段的参数模型和聚类方法实现道路的检测，尤其针对复杂的车道几何结构和曲率，同时输出一个基于学习的检测不确定性评估，实验证明效果显著。