Abstract Accurate registration of high-resolution multispectral UAV orthomosaics acquired on different dates and sensors is essential for a wide range of remote sensing applications. However, this task remains challenging due to variations in acquisition conditions, including seasonal changes, differences in illumination, weather, and sensor characteristics. This article presents a parallel multilevel registration method that combines and improves two existing algorithms: HSI-KAZE, a feature-based approach, and HYFM, an area-based method. The three-level approach first applies an optimized HSI-KAZE (OHSI-KAZE) for coarse estimation of scale, rotation, and translation, followed by HYFM for fine correction. The multi-node multi-GPU proposed implementation, leveraging MPI, OpenMP, and CUDA, enables efficient processing on GPU-accelerated HPC clusters. Experiments on six real multispectral orthomosaic pairs from river environments, compared against state-of-the-art classical and deep learning methods, achieve high registration accuracy with RMSE values below 1.57 pixels and a 30 $$\times $$ × speedup, confirming both the accuracy and scalability of the proposed method.

The monitoring of semi-natural ecosystems has become increasingly critical due to the rising impact of ecological disturbances, including natural disasters and unauthorized human-made constructions. Anomaly detection (AD) in multispectral imagery serves as a fundamental tool in this context. Deep learning-based techniques are particularly effective at capturing the intricate spectral and spatial patterns of anomalies. This paper proposes a new AD technique called ACNN, designed to enhance AD performance in multispectral images of high spatial resolution for the detection of human-made constructions. The model integrates attention mechanisms to prioritize informative features while suppressing irrelevant background information, thereby improving sensitivity to subtle and rare anomalies. Experimental results on multispectral datasets from semi-natural ecosystems show that the proposed approach outperforms existing deep learning (DL) techniques in terms of detection accuracy. These findings highlight the potential of attention-based models as a robust framework for environmental monitoring and AD in complex remote sensing scenarios.

Feature-based methods are widely used for the registration of remote sensing images because of their robustness to viewpoint, scale, and light changes. However, they are computationally demanding, especially when dealing with multi or hyperspectral images. Hyperspectral Maximally Stable Extremal Regions (HSI-MSER) is a hyperspectral remote sensing image registration method based on MSER for feature detection and Scale Invariant Feature Transform (SIFT) for feature description. This article presents a first approach to a parallel implementation of the HSI-MSER algorithm for the registration of multispectral images on a heterogeneous computing platform. The results of the registration capabilities under extreme scaling and rotating conditions show that the proposed parallel implementation obtains a speedup of 3.33× compared to the sequential implementation making it suitable for applications with execution time constraints.

Image registration is a crucial process that involves determining the geometric transformation required to align multiple images. It plays a vital role in various remote sensing image processing tasks that involve analyzing changes among images. To enable real-time response, it is essential to have computationally efficient registration algorithms, especially when dealing with large datasets as is the case of hyperspectral images. This article presents a comparative analysis of two descriptors used to characterize local features of images prior to their matching and registration. The objective is to analyze whether the LATCH binary keypoint descriptor, which produces compact descriptors, provides similar results to the gradient-based SURF descriptor in terms of execution time and registration precision. To obtain the best computational performance, multithreaded implementations using OpenMP have been proposed. LATCH has proven to be 7× faster and as reliable as SURF in terms of accuracy on scale differences of up to 1.2×.

Feature-based registration methods have been demonstrated to be very effective to register multispectral images with large distortions or transformations despite the higher execution time that they require.In this paper, a first approach to a multi-node, multi-GPU implementation of the Hyperspectral KAZE (HSI-KAZE) method for co-registering bands and multispectral images is presented.Different multispectral datasets are distributed among the available nodes of a cluster using MPI and exploiting the parallel stream-based capabilities of the GPUs inside each node using CUDA.

Registration is required as a previous step for processing multispectral images. The different bands captured by each sensor for each image, as well as the different images corresponding to the same area, need to be aligned. In this paper, a 2-level registration scheme comparing the results obtained by the hyperspectral Fourier-Mellin (HYFM) and hyperspectral KAZE (HSI-KAZE) registration methods is proposed. It is designed for efficient implementation in a multi-GPU system in which different scenes are registered in parallel on different GPUs.

The alignment of images, also known as registration, is a relevant task in the processing of hyperspectral images. Among the feature-based registration methods, Speeded Up Robust Features (SURF) has been proposed as a computationally efficient approach. In this paper HSI-SURF is proposed. This is a method to register hyperspectral remote sensing images based on SURF that takes advantage of the full spectral information of the images. In this sense, the proposed method selects specific bands of the images and adapts the keypoint descriptor and the matching stages to benefit from the spectral information, thus increasing the effectiveness of the registration.