In this paper, we propose a promising approach for the application-oriented content classification of space-borne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is an increasing demand towards explainable machine learning models as they strive to meet the "right to explanation". The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labelled data and the dependence on features. In this paper, we demonstrate the applicability of a Latent Dirichlet Allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar (SAR) data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced which practically demonstrate the idea of "interpretability" in an explainable machine learning paradigm. LDA discovers the latent structures in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class. Our results show that each class has a distinct topic distribution that represents that particular class. Then these classes can be grouped based on their similarity of topic composition. Both the topic composition and grouping are explainable by domain experts.
Openly available satellite image time series (SITS) are considered an important resource for spatiotemporal change monitoring. However, obtaining semantically annotated datasets for such tasks is an expensive affair. To alleviate this problem, this article presents a novel framework to model and understand the image dynamics by discovering latent information in Sentinel-1 SITS, even with limited ground truth data. The framework suggests how to use visualizations to efficiently integrate domain knowledge both for execution and evaluation of the machine-learning pipeline in the absence from ground truth data in SITS change studies. In a case study at a Polar region, we extend a limited amount of ground truth data and then discover its temporal evolution at image patch level, in an unsupervised manner. The trustworthiness of the framework is ensured by integration of domain knowledge and intelligent visual verification strategies. A visualization tool is also implemented for this purpose. The proposed framework contains two modules: a classifier and a change modeler. Our experiments show that a domain-knowledge-based classifier gives the best accuracy. The classifier semantically labeled the complete dataset of 24 study months, containing 153 600 patches with a size of 256 × 256 pixels by extending the available semantic labels from just three months. The temporal sequence of these sematic labels are then recorded and fed to a Bayesian model called Latent Dirichlet Allocation (LDA) to discover the underlying patterns. LDA generates a change map containing the dominant dynamic patterns to give a consolidated view of the evolution without having to browse the whole dataset. Further, color-coded change signatures explain the change classes.
<p>The European Copernicus Sentinel-1 SAR mission offers a unique chance to compare and analyse long time series of freely accessible SAR images with frequent coverage in the northern polar areas. In our case, during the ExtremeEarth project (H2020 grant agreement No 825258), we concentrated on a two-year analysis of multi-season ice cover categories around Belgica Bank in Greenland where we can easily use typical examples of SAR image targets ranging from snow-covered ice to melting ice surfaces as well as open sea scenes with ships and icebergs.</p> <p>Our primary goal was to search for most powerful ice type classification algorithms exploiting the well-known characteristics of the Sentinel-1 satellites for SAR imaging in polar areas, both taken from ascending and descending orbit branches with C-band transmission and an incidence angle of about 39&#176;, a resulting ground sampling distance of 10 m or more, HH or HV polarization, and recorded in wide-swath or high-resolution modes as provided and distributed routinely by ESA&#180;s level-1 processing system as amplitude or complex-valued data.</p> <p>In order to be compatible with established international ice type standards we used the Canadian MANICE semantic labelling system providing up to 10 different polar ice and polar target types.</p> <p>Our algorithms are based on a patch-based classification approach, where we assigned the most probable primary label for each given square image patch with a size of 256&#215;256 pixels. This prevented us from creating many noise-related single-pixel categories.</p> <p>Within the ExtremeEarth project, were generated semantic classification maps, topic representations, change maps, or physical scattering representations. A library of algorithms was created, among these algorithms we mention the following ones: classification based on Gabor filtering and SVMs, classification based on compression rates, variational auto-encoders for SAR feature learning, topic representations based on LDA, physical scattering representations based on LDA and CNNs, etc.</p> <p>When the attempted image content classification based on current machine learning approaches, it turned out that we had to consider several important parameters such as typical applications, main semantic goals to be reached, applied processing algorithms, common types of data, available datasets and already predefined categories to be used, pixel-based versus patch-based data processing, single- and multi-labelling of image patches, confidence calculations and annotations, as well as attainable runtimes, implementation effort and risk - all depending on the target area characteristics. When it came to time series of target area images, we also had to consider the chances offered by short and long data sequences.</p> <p>It turned out that this large number of aspects can be grouped together depending on the applied human expert supervision approach for semantic classification, namely unsupervised, self-supervised, semi-supervised, and supervised algorithms together with their individual training and testing strategies. In future, we want provide some justifications for next-generation remote sensing applications that require (near) real-time capabilities.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.