1 research outputs found
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?
Recently, deep convolutional neural network (DCNN) achieved increasingly
remarkable success and rapidly developed in the field of natural image
recognition. Compared with the natural image, the scale of remote sensing image
is larger and the scene and the object it represents are more macroscopic. This
study inquires whether remote sensing scene and natural scene recognitions
differ and raises the following questions: What are the key factors in remote
sensing scene recognition? Is the DCNN recognition mechanism centered on object
recognition still applicable to the scenarios of remote sensing scene
understanding? We performed several experiments to explore the influence of the
DCNN structure and the scale of remote sensing scene understanding from the
perspective of scene complexity. Our experiment shows that understanding a
complex scene depends on an in-depth network and multiple-scale perception.
Using a visualization method, we qualitatively and quantitatively analyze the
recognition mechanism in a complex remote sensing scene and demonstrate the
importance of multi-objective joint semantic support