An official website of the United States government
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.
more »
« less
Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification
A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm designed to be applied to such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth’s climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms.
more »
« less
- Award ID(s):
- 2003740
- PAR ID:
- 10433822
- Date Published:
- Journal Name:
- SoutheastCon 2023
- Page Range / eLocation ID:
- 175 to 178
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The CloudPatch-7 Hyperspectral Dataset comprises a manually curated collection of hyperspectral images, focused on pixel classification of atmospheric cloud classes. This labeled dataset features 380 patches, each a 50x50 pixel grid, derived from 28 larger, unlabeled parent images approximately 5000x1500 pixels in size. Captured using the Resonon PIKA XC2 camera, these images span 462 spectral bands from 400 to 1000 nm. Each patch is extracted from a parent image ensuring that its pixels fall within one of seven atmospheric conditions: Dense Dark Cumuliform Cloud, Dense Bright Cumuliform Cloud, Semi-transparent Cumuliform Cloud, Dense Cirroform Cloud, Semi-transparent Cirroform Cloud, Clear Sky - Low Aerosol Scattering (dark), and Clear Sky - Moderate to High Aerosol Scattering (bright). Incorporating contextual information from surrounding pixels enhances pixel classification into these 7 classes, making this dataset a valuable resource for spectral analysis, environmental monitoring, atmospheric science research, and testing machine learning applications that require contextual data. Parent images are very big in size, but they can be made available upon request.more » « less
-
null (Ed.)We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. Indeed, the proposed method not only achieves excellent labeling accuracy, but also efficiently estimates the number of clusters. Thus, unlike almost all existing hyperspectral clustering methods, the proposed algorithm is essentially parameter-free.more » « less
-
The problem of classifying multiple categorical responses is fundamental in modern machine learning and statistics, with diverse applications in fields such as bioinformatics and imaging. This manuscript investigates linear discriminant analysis (LDA) with high-dimensional predictors and multiple multi-class responses. Specifically, we first examine two different classification scenarios under the bivariate LDA model: joint classification of the two responses and conditional classification of one response while observing the other. To achieve optimal classification rules for both scenarios, we introduce two novel tensor formulations of the discriminant coefficients and corresponding regularization strategies. For joint classification, we propose an overlapping group lasso penalty and a blockwise coordinate descent algorithm to efficiently compute the joint discriminant coefficient tensors. For conditional classification, we utilize an alternating direction method of multipliers (ADMM) algorithm to compute the discriminant coefficient tensors under new constraints. We then extend our method and algorithms to general multivariate responses. Finally, we validate the effectiveness of our approach through simulation studies and applications to benchmark datasets.more » « less
-
Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SoutheastCon51012.2023.10115209
