Multi-Scale CNN Based Garbage Detection of Airborne Hyperspectral Data

IEEE Access

Fansheng Chen¹  Yueming Wang²  Shun Zhang³  Dan Zeng³ 

[1] Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai, China;Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China;Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai, China;

关键词: Airborne;    hyperspectral image classification;    multi-scale convolutional neural network;    garbage detection;   

DOI  :  10.1109/ACCESS.2019.2932117

来源: DOAJ

【 摘 要 】

Garbage detection is important for environmental monitoring in large areas. However, the manual patrol is time-consuming and labor-intensive. This paper proposes a method for monitoring garbage distribution in large areas with airborne hyperspectral data. Since there is no public hyperspectral garbage dataset, a hyperspectral garbage dataset Shandong Suburb Garbage is labeled and published. For garbage detection, a new hyperspectral image (HSI) classification network MSCNN (Multi-Scale Convolutional Neural Network) is proposed to classify the pixels of HSI data and generate binary garbage segmentation map. Unsupervised region proposal generation algorithm Selective Search and None Maximum Suppression (NMS) are used to extract the location and the size of garbage areas based on the garbage segmentation map. The experiment results show that the proposed algorithm has a good performance on garbage detection in large areas. In addition, the MSCNN has achieved better performance in comparison with other HSI classification methods in the public HSI datasets Indian Pines and Pavia University.

【 授权许可】

Unknown