Hyperspectral Imaging Retrieval Using MODIS Satellite Sensors Applied to Volcanic Ash Clouds Monitoring

Abstract

In this paper, we present a method for hyperspectral retrieval using multispectral satellite images. The method consists of the use of training spectral data with a compressive capability. By using principal component analysis (PCA), a proper number of basis vectors are extracted. These vectors are properly combined and weighted by the sensors’ responses from visible MODIS channels, achieving as a result the retrieval of hyperspectral images. Once MODIS channels are used for hyperspectral retrieval, the training spectra are projected over the recovered data, and the ground-based process used for training can be reliably detected. To probe the method, we use only four visible images from MODIS for large-scale ash clouds’ monitoring from volcanic eruptions. A high-spectral resolution data of reflectances from ash was measured in the laboratory. Using PCA, we select four basis vectors, which combined with MODIS sensors responses, allows estimating hyperspectral images. By comparing both the estimated hyperspectral images and the training spectra, it is feasible to identify the presence of ash clouds at a pixel-by-pixel level, even in the presence of water clouds. Finally, by using a radiometric model applied over hyperspectral retrieved data, the relative concentration of the volcanic ash in the cloud is obtained. The performance of the proposed method is compared with the classical method based on temperature differences (using infrared MODIS channels), and the results show an excellent match, outperforming the infrared-based approach. This proposal opens new avenues to increase the potential of multispectral remote systems, which can be even extended to other applications and spectral bands for remote sensing. The results show that the method could play an essential role by providing more accurate information of volcanic ash spatial dispersion, enabling one to prevent several hazards related to volcanic ash where volcanoes’ monitoring is not feasible.

Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

“…Remote sensing data and techniques have already seen some use in assessing the impact of earthquakes (Fu et al 2005;Klinger 2005;Douglas 2007;Yamazaki and Matsuoka 2007;Dong and Shan 2013;Geiß and Taubenböck 2013;Taubenböck et al 2014;Wu et al 2014;Wyss 2014;Geiß et al 2015;Platt et al 2016;Anniballe et al 2018), and volcanic eruptions (Carn et al 2009;Ferguson et al 2010;Arias et al 2019). The authors of this article have also used remote sensing techniques for both disaster types before (Skoufias et al 2017;Tveit 2017).…”

“…Wilson et al (2014) provided a detailed overview of the damages caused by volcanic eruptions to infrastructure. Remote sensing of volcanic impacts is focused mostly on ash clouds and their monitoring (Carn et al 2009;Ferguson et al 2010;Arias et al 2019). The primary focus of these studies was detection and modeling of the aviation industry impact, whereas in this study we used the data as a proxy for local damages.…”

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

“…Today, hyperspectral remote sensing has played a critically important role in many fields, such as mineral detection [2], ecological environment investigation and detection [3] (water environment [4], air pollution [5], soil erosion [6], vegetation detection [7], etc. ), dynamic monitoring of marine [8], atmospheric [9], land [10], and military application [11].…”

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

“…With the development of satellite technology, the quality of remote sensing (RS) images increases dramatically. Retrieving similar RS images from large-scale RS datasets is very important and demanding [1][2][3]. Interestingly, content-based image retrieval (CBIR) [4][5][6] is widely involved in many real-world tasks, such as natural image retrieval and network searches.…”