Accelerated Extinction Profiles for Anomaly Detection in Fluvial Ecosystems

New multispectral sensors, which are capable of capturing high resolution images through low altitude drone flights, offer access to a wealth of information about the Earth's surface at a significantly lower cost than other imaging devices. The process of identifying unexpected patterns within an image that do not conform to the expected behavior is known as anomaly detection (AD). When applied to fluvial ecosystem monitoring, this involves detecting the existence of small constructions or roads that allow automatic alarms to be produced for the people in charge of monitoring the ecosystem. The extraction of spatial information is a critical step in AD, since it determines the final quality of the AD and it is a computationally expensive processing. In this work, Extinction Profiles (EP) are selected to perform a multilevel implicit segmentation of the image, thus extracting the spatial information of relevance. A computationally efficient implementation of the EP-based spatial extraction of information for multidimensional images is proposed in this paper, as it is a basic step in the detection of anomalies in natural ecosystems. The proposed method takes advantage of heterogeneous computing to perform the task in a reduced execution time.This work was supported in part by grants PID2019--104834GB--I00, PID2022-141623NB-I00, and TED2021--130367B--I00 funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR. It was also supported by Xunta de Galicia - Consellería de Cultura, Educación, Formación Profesional e Universidades [Centro de investigación de Galicia accreditation 2019-2022 ED431G-2019/04 and Reference Competitive Group accreditation, ED431C-2022/16], by Junta de Castilla y León [Project VA226P20 (PROPHET--II)], and by European Regional Development Fund (ERDF)

GPU computation of Attribute Profiles for Remote Sensing Image Classification

Classification of multi and hyperspectral remote sensing images is a common task. It usually requires a previous step consisting of a technique for extracting the spatial information from the image, being profiles a common approach. In particular, attribute profiles are based on the application of a morphological filter to the connected components of the image producing rel-evant spatial information at different levels of detail. The information is built based on attributes such as area or standard deviation. Their high computa-tional cost makes the attribute profiles good candidates for their execution on commodity GPUs. In this paper, the first parallel implementation of attribute profiles over multispectral images in CUDA for Nvidia GPUs is proposed. The GPU proposal is based on the construction of a max-tree that is traversed from the leaves to the root by merging the connected components of the tree obtaining a considerable reduction in execution time over the CPU execution

A New Multispectral Data Augmentation Technique Based on Data Imputation

Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out

A New Multispectral Data Augmentation Technique Based on Data Imputation

Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out

GPU Framework for Change Detection in Multitemporal Hyperspectral Images

International audienc

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precision

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article