In the past decade, deep learning methods have proven to be highly effective in the classification of hyperspectral images (HSI), consistently outperforming traditional approaches. However, the large number of spectral bands in HSI data can lead to interference during the learning process. To address this issue, dimensionality reduction techniques can be employed to minimize data redundancy and improve HSI classification performance. Hence, we have developed an efficient lightweight learning framework consisting of two main components. Firstly, we utilized band selection and principal component analysis to reduce the dimensionality of HSI data, thereby reducing redundancy while retaining essential features. Subsequently, the pre-processed data was input into a modified VGG-based learning network for HSI classification. This method incorporates an improved dynamic activation function for the multi-layer perceptron to enhance non-linearity, and reduces the number of nodes in the fully connected layers of the original VGG architecture to improve speed while maintaining accuracy. This modified network structure, referred to as lightweight-VGG (LVGG), was specifically designed for HSI classification. Comprehensive experiments conducted on three publicly available HSI datasets consistently demonstrated that the LVGG method exhibited similar or better performance compared to other typical methods in the field of HSI classification. Our approach not only addresses the challenge of interference in deep learning methods for HSI classification, but also offers a lightweight and efficient solution for achieving high classification accuracy.

As a pivotal strategy to deal with complicated and high-dimensional data, subspace clustering is to find a set of subspaces of a high-dimensional space and then partition each data point in dataset into the corresponding subspace. This field has witnessed remarkable progress over recent decades, with substantial theoretical advancements and successful applications spanning image processing, genomic analysis and text analysis. However, existing surveys predominantly focus on conventional shallow-structured methods, with few up-to-date reviews on deep-structured methods, i.e., deep neural network-based approaches. In fact, recent years has witnessed the overwhelming success of deep neural network in various fields, including computer vision, natural language processing, subspace clustering. To address this gap, this paper presents a comprehensive review on subspace clustering methods, including conventional shallow-structured and deep neural network based approaches, which systematically analyzes over 150 papers published in peer-reviewed journals and conferences, highlighting the latest research achievements, methods, algorithms and applications. Specifically, we first briefly introduce the basic principles and evolution of subspace clustering. Subsequently, we present an overview of research on subspace clustering, dividing the existing works into two categories: shallow subspace clustering and deep subspace clustering, based on the model architecture. Within each category, we introduce a refined taxonomy distinguishing linear and nonlinear approaches based on data characteristics and subspace structural assumptions. Finally, we discuss the challenges currently faced and future research direction for development in the field of subspace clustering.