Deep learning-based intrinsic image decomposition (IID) has gained significant attention in computer vision due to the high efficiency and accuracy of learning-based methods. However, the development of deep learning-based IID methods in the remote sensing field has been limited by the lack of experimental datasets. This article proposes a two-stream encoder-decoder network for the single hyperspectral (HS) image IID task. The proposed network comprises one reflectance estimation subnetwork and one shading estimation subnetwork, which predict intrinsic properties separately. The proposed model introduces three physical losses to enhance performance: 1) In the reflectance estimation subnetwork, the self-similarity loss on the reflectance component is added to satisfy the basic assumption that pixels with similar intensity tend to have a similar reflectance property. 2) In the shading estimation subnetwork, the shading structure loss is added to ensure that the structure of the shading component conforms to physical observation. 3) Reconstruction loss connecting two subnetworks is required to ensure the estimated intrinsic components are physically correct. Finally, to avoid an unreasonable composition, the entire network is initialized by reflectance estimated by the physical model. The quantitative experimental results of intraclass consistency and classification metrics demonstrate that the proposed physical prior-driven unsupervised learning-based IID network outperforms the current available learning or optimization-based approaches.

Generating hyperspectral point cloud from hyperspectral images (HSIs) and light detection and ranging (LiDAR) has become more and more common in the remote sensing field and supported various applications. One challenge here is that hyperspectral imaging is a passive imaging method and is suffering from shadows in a natural scene. Intrinsic information recovery can effectively eliminate the spectral variation caused by illumination changes; however, it assumes a uniform light and neglects the shadows in the scene. In this article, we provide a novel hyperspectral point cloud intrinsic model that can detect the shaded regions and recover reflectance information in them. We first estimate the global illumination of the scene using an intrinsic information recovery method. Then, we perform supervoxel segmentation on hyperspectral point cloud to calculate the blocking relation of supervoxels and therefore accurately detect shaded regions. Finally, we estimate the illumination and reflectance of shaded regions based on an illumination-invariant spectral prior. The experimental results show that the proposed method can effectively detect shaded areas and robustly generate shadow-less intrinsic hyperspectral point cloud.

Hyperspectral intrinsic image decomposition (HyperIID) has been proven to be a very useful approach to reduce the spectral uncertainty in the remote sensing imaging process and improve the classification. In this article, a new HyperIID with enhanced spatial information, called ESI-IID, is proposed to overcome the deficiency of low spatial resolution in the existing HyperIID methods. With the aid of high-resolution (HR) panchromatic (PAN) image, the proposed method embeds the HR spatial information into the intrinsic decomposition model and enhances spatial details of the intrinsic component. The proposed ESI-IID introduces three constraints: 1) we make the constraint on spectral information to protect it from distortion during the spatial resolution enhancement process; 2) we add the constraint on spatial information to make sure that the details of edges will be well kept; and 3) based on the assumption that the reflectance component has a strong correlation in the local neighborhood, we add the self-constraint on reflectance component, in which the similarity matrix consists of two parts extracted from hyperspectral images and PAN image, respectively. Finally, we build a matrix energy function according to the aforementioned constraints and solve it by finding the minimum Frobenius norm iteratively. Both visual and quantitative experiments on simulated and real datasets demonstrate that the proposed method outperforms other alternative methods with high reliability.

The combination of spectral and 3-D elevation information provided by hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) has gained increased attention in the remote sensing field and enabled numerous applications. While various methods have been proposed to fuse these two data streams in pixel, feature, or decision level, a deeper view into the intrinsic relation of surface geometry, material reflectance, and environment illumination is still lacking. In this article, we present a novel supervoxel-based joint intrinsic decomposition framework for HSIs and LiDAR. First, we proposed a novel intrinsic scene model for HSIs and LiDAR point cloud, which tells how we can map LiDAR point cloud into HSI pixels with point-cloud-level normals, reflectance, and incident light direction. Then, we extract supervoxels from the LiDAR point cloud using a graph-based supervoxel method. Finally, we formulate the intrinsic decomposition problem within a supervoxel-based framework which can be optimized effectively and efficiently. The outputs of the proposed model are intrinsic scene properties like incident light direction and point-cloud-level hyperspectral reflectance, with which we can then generate intrinsic hyperspectral point cloud (IHSPC) where each point possesses not only 3-D coordinates and normals but also the reflectance over each wavelength. The performance of our approach is demonstrated with both synthetic and real data.