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

In this paper, automatic extraction of multi-context and multi-scale land use/land cover vegetation from high-resolution remote sensing images is tackled, aiming to solve typical challenges in classifying remote sensing images at a pixel level. To solve small inter-class differences and large intra-class differences between the vegetation and background, we introduce a vegetation-feature-sensitive focus perception (FP) module. Considering the intrinsic properties of vegetation objects, we established an adaptive context inference (ACI) model under a supervised setting that includes a context model to represent relationships between a center pixel and its neighbors under semantic constraints, as well as the spatial structures of vegetation features. Comparative experiments on the ZY-3 and Gaofen Image Dataset (GID) datasets demonstrate the effectiveness of our proposed automatic vegetation extraction model against the baseline Deeplab v3&#x002B; model. Taking precision, kappa coefficient, mean intersection over union (miou), precision rate, and F1-score as the evaluation indexes, the results showed an improvement in the precision by at least 1.44&#x0025; and miou by 2.47&#x0025;, over the baseline Deeplab v3&#x002B; model. In addition, the ACI module improved the precision and miou by 2&#x0025; and 3.88&#x0025;, and the FP module improved the precision and miou by 1.13&#x0025; and 1.65&#x0025;. These results and statistics of these comprehensive experiments illustrated that our adaptive and effective vegetation extraction model could satisfy different requirements of land use/land cover mapping applications.

Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference