Hyperspectral image classification is an attractive and challenging task due to the difficulty to acquire large labeled datasets, and its susceptibility to natural environmental influences. Deep learning-based methods can effectively enhance the efficiency of hyperspectral feature learning. Tensor-based approaches can effectively help preserving the inherent structural information. This paper introduces a novel tensor-based framework for hyperspectral image classification. It leverages the ‘bot-tleneck’ structure of an AutoEncoder to extract low-dimensional representations in a self-supervised manner. Additionally, local low-rank constraints in the embedding space facilitate the distribution of features across a low-rank manifold. Experiments conducted on real hyperspectral datasets demonstrate that the proposed method yields superior classification performance.

In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However, they are struggling to generate shadows with accurate shapes and intensities, hindered by data scarcity and inherent task complexity. In this paper, we resort to foundation model with rich prior knowledge of natural shadow images. Specifically, we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover, we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset, code, and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.