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1. Clustering Augmented Self-Supervised Learning: An Application to Land Cover Mapping

   Ghosh, Rahul; Jia, Xiaowei; Chenxi, Lin; Jin, Zhenong; Kumar, Vipin (August 2021, DEEPSPATIA 2021: 2nd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems)

   null (Ed.)

   Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without using any human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering-based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning, and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget of annotating. 

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2. A Manifold Laplacian Regularized Semi-supervised Sparse Image Classification Method with a Variant Trace Lasso Norm

   https://doi.org/DOI: 10.1109/ACCESS.2020.2997413  

   Zhang, Wenjuan; Feng, Xiangchu; Chen, Yunmei (May 2020, IEEE access)

   Since the cost of labeling data is getting higher and higher, we hope to make full use of the large amount of unlabeled data and improve image classification effect through adding some unlabeled samples for training. In addition, we expect to uniformly realize two tasks, namely the clustering of the unlabeled data and the recognition of the query image. We achieve the goal by designing a novel sparse model based on manifold assumption, which has been proved to work well in many tasks. Based on the assumption that images of the same class lie on a sub-manifold and an image can be approximately represented as the linear combination of its neighboring data due to the local linear property of manifold, we proposed a sparse representation model on manifold. Specifically, there are two regularizations, i.e., a variant Trace lasso norm and the manifold Laplacian regularization. The first regularization term enables the representation coefficients satisfying sparsity between groups and density within a group. And the second term is manifold Laplacian regularization by which label can be accurately propagated from labeled data to unlabeled data. Augmented Lagrange Multiplier (ALM) scheme and Gauss Seidel Alternating Direction Method of Multiplier (GS-ADMM) are given to solve the problem numerically. We conduct some experiments on three human face databases and compare the proposed work with several state-of-the-art methods. For each subject, some labeled face images are randomly chosen for training for those supervised methods, and a small amount of unlabeled images are added to form the training set of the proposed approach. All experiments show our method can get better classification results due to the addition of unlabeled samples. 

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3. Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

   https://doi.org/10.3390/rs15041053  

   Polk, Sam L.; Cui, Kangning; Chan, Aland H.; Coomes, David A.; Plemmons, Robert J.; Murphy, James M. (February 2023, Remote Sensing)

   Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets. 

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4. Collection of a Hyperspectral Atmospheric Cloud Dataset and Enhancing Pixel Classification through Patch-Origin Embedding

   https://doi.org/10.3390/rs16173315  

   Yan, Hua; Zheng, Rachel; Mallela, Shivaji; Russell, Randy; Kursun, Olcay (September 2024, Remote Sensing)

   Hyperspectral cameras collect detailed spectral information at each image pixel, contributing to the identification of image features. The rich spectral content of hyperspectral imagery has led to its application in diverse fields of study. This study focused on cloud classification using a dataset of hyperspectral sky images captured by a Resonon PIKA XC2 camera. The camera records images using 462 spectral bands, ranging from 400 to 1000 nm, with a spectral resolution of 1.9 nm. Our preliminary/unlabeled dataset comprised 33 parent hyperspectral images (HSI), each a substantial unlabeled image measuring 4402-by-1600 pixels. With the meteorological expertise within our team, we manually labeled pixels by extracting 10 to 20 sample patches from each parent image, each patch consisting of a 50-by-50 pixel field. This process yielded a collection of 444 patches, each categorically labeled into one of seven cloud and sky condition categories. To embed the inherent data structure while classifying individual pixels, we introduced an innovative technique to boost classification accuracy by incorporating patch-specific information into each pixel’s feature vector. The posterior probabilities generated by these classifiers, which capture the unique attributes of each patch, were subsequently concatenated with the pixel’s original spectral data to form an augmented feature vector. We then applied a final classifier to map the augmented vectors to the seven cloud/sky categories. The results compared favorably to the baseline model devoid of patch-origin embedding, showing that incorporating the spatial context along with the spectral information inherent in hyperspectral images enhances the classification accuracy in hyperspectral cloud classification. The dataset is available on IEEE DataPort. 

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5. JECL: Joint Embedding and Cluster Learning for Image-Text Pairs

   https://doi.org/10.1109/ICPR48806.2021.9412667  

   Yang, Sean T.; Huang, Kuan-Hao; Howe, Bill (January 2021, 2020 25th International Conference on Pattern Recognition (ICPR))

   null (Ed.)

   We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is expensive to produce, but free-text descriptions are common. JECL trains by minimizing the Kullback-Leibler divergence between the distribution of the images and text to that of a combined joint target distribution and optimizing the Jensen-Shannon divergence between the soft cluster assignments of the images and text. Regularizers are also applied to JECL to prevent trivial solutions. Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes. 

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