Hyperspectral image (HSI) clustering is a challenging task due to its high
complexity. Despite subspace clustering shows impressive performance for HSI,
traditional methods tend to ignore the global-local interaction in HSI data. In
this study, we proposed a multi-level graph subspace contrastive learning
(MLGSC) for HSI clustering. The model is divided into the following main parts.
Graph convolution subspace construction: utilizing spectral and texture
feautures to construct two graph convolution views. Local-global graph
representation: local graph representations were obtained by step-by-step
convolutions and a more representative global graph representation was obtained
using an attention-based pooling strategy. Multi-level graph subspace
contrastive learning: multi-level contrastive learning was conducted to obtain
local-global joint graph representations, to improve the consistency of the
positive samples between views, and to obtain more robust graph embeddings.
Specifically, graph-level contrastive learning is used to better learn global
representations of HSI data. Node-level intra-view and inter-view contrastive
learning is designed to learn joint representations of local regions of HSI.
The proposed model is evaluated on four popular HSI datasets: Indian Pines,
Pavia University, Houston, and Xu Zhou. The overall accuracies are 97.75%,
99.96%, 92.28%, and 95.73%, which significantly outperforms the current
state-of-the-art clustering methods.

本文提出了一种用于高光谱图像 (HSI) 聚类的多级图像子空间对比学习 (MLGSC) 模型，通过构建图卷积子空间、局部全局图表示和多级对比学习，有效提高了聚类准确性。在四个常见的 HSI 数据集上进行评估，结果显示该模型的整体准确率显著优于当前最先进的聚类方法。

多层次图子空间对比学习用于高光谱图像聚类

Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image  Clustering

In this work, we present Multi-Level Contrastive Learning for Dense
Prediction Task (MCL), an efficient self-supervised method for learning
region-level feature representation for dense prediction tasks. Our method is
motivated by the three key factors in detection: localization, scale
consistency and recognition. To explicitly encode absolute position and scale
information, we propose a novel pretext task that assembles multi-scale images
in a montage manner to mimic multi-object scenarios. Unlike the existing
image-level self-supervised methods, our method constructs a multi-level
contrastive loss that considers each sub-region of the montage image as a
singleton. Our method enables the neural network to learn regional semantic
representations for translation and scale consistency while reducing
pre-training epochs to the same as supervised pre-training. Extensive
experiments demonstrate that MCL consistently outperforms the recent
state-of-the-art methods on various datasets with significant margins. In
particular, MCL obtains 42.5 AP$^\mathrm{bb}$ and 38.3 AP$^\mathrm{mk}$ on COCO
with the 1x schedule fintuning, when using Mask R-CNN with R50-FPN backbone
pre-trained with 100 epochs. In comparison to MoCo, our method surpasses their
performance by 4.0 AP$^\mathrm{bb}$ and 3.1 AP$^\mathrm{mk}$. Furthermore, we
explore the alignment between pretext task and downstream tasks. We extend our
pretext task to supervised pre-training, which achieves a similar performance
to self-supervised learning. This result demonstrates the importance of the
alignment between pretext task and downstream tasks, indicating the potential
for wider applicability of our method beyond self-supervised settings.

本文介绍了一种名为 Multi-Level Contrastive Learning for Dense Prediction Task  (MCL) 的自监督学习方法，该方法通过实现一种装配多尺度图像的贡献任务，显式地编码绝对位置和比例信息，从而使神经网络能够学习区域语义表示。实验表明，MCL 在各种数据集上始终优于最新的现有方法。