This paper presents the novel observation model, called Modified Spherical Signature Descriptor(MSSD), capable of representing 2D image generated from 3D point cloud data. The Modified Spherical Signature Descriptor has a uniform mesh grid to accumulate the occupancy evidence caused by neighbor point cloud data. According to a kind of area such as wall, road, tree, car, and so on, the evidence pattern of 2D image looks so different each other. For the parameter learning of Convolutional Neural Network(CNN) layers, these 2D images were applied as the input layer. The Convolutional Neural Network, one of the deep learning methods and familiar with the image analysis, was utilized for the urban structure classification. The case study on CNN practice was introduced in detail in this paper. The simulation results shows that the classification accuracy of CNN with 2D images of the proposed MSSD was improved more than the traditional methods' one.

1. 서 론
  1.1 연구 배경
  1.2 연구 동향
 2. 수정 구형 특징 표현기
  2.1 문제 정의
  2.2 격자 점유 확률 갱신
 3. 컨볼루션 신경망
 4. 실 험
  4.1 실험 데이터
  4.2 모델 파라미터
  4.3 실험 결과
 5. 결 론
 후 기
 References

• 이언호(Department of Mechanical Engineering, Kongju National University) | Eon-Ho Lee
• 이세진(Division of Mechanical and Automotive Engineering, Kongju National University) | Se-Jin Lee Corresponding Author