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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, deep convolutional neural network (DCNN) achieved increasinglyremarkable success and rapidly developed in the field of natural imagerecognition. Compared with the natural image, the scale of remote sensing imageis larger and the scene and the object it represents are more macroscopic. Thisstudy inquires whether remote sensing scene and natural scene recognitionsdiffer and raises the following questions: What are the key factors in remotesensing scene recognition? Is the DCNN recognition mechanism centered on objectrecognition still applicable to the scenarios of remote sensing sceneunderstanding? We performed several experiments to explore the influence of theDCNN structure and the scale of remote sensing scene understanding from theperspective of scene complexity. Our experiment shows that understanding acomplex scene depends on an in-depth network and multiple-scale perception.Using a visualization method, we qualitatively and quantitatively analyze therecognition mechanism in a complex remote sensing scene and demonstrate theimportance of multi-objective joint semantic support.

What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?