English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Land cover classification plays a pivotal role in Earth resource management. In the past, synthetic aperture radar (SAR) had been extensively studied for classification. However, limited work has been done on multi-temporal datasets owing to the lack of data availability and computational power. As Earth observation (EO) becomes more and more imperative, it becomes essential to exploit the information embedded in multi-temporal datasets. In this paper, we present a framework for SAR pixel labeling. Specifically, we exploit spatio-temporal information for pixel labeling. The proposed scheme includes four steps: (1) extraction of spatio-temporal observations; (2) feature computation; (3) feature reduction and (4) pixel labeling. First, an adaptive approach is applied to the data cube to extract spatio-temporal observations in both coherent and incoherent domains. Second, features in distinct domains are designed and computed to boost information content embedded in the multi-temporal datasets. Third, sequential feature selection is utilized for selecting the most discriminative features among the entire feature space. Last, the discriminative classifier is used to label the class of each pixel. By integrating pixel-/object-based processing techniques, spatial/temporal observations and coherent/incoherent data attributes, the proposed method explores diverse observations to solve complex labeling problems. In the experiments, we apply the proposed method on 64 TanDEM-X images and 70 COSMO-SkyMed high-resolution images, respectively. Both experiments reveal high accuracies for multi-class labeling. The proposed technique, therefore, provides a new solution for classifying multi-temporal single-polarized datasets.

Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack