Abstract
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.
Highlights
In recent decades, the rapid development of economy has brought environmental pollution, especially for developing countries
The filter weights of MSCNN are initialized by Gaussian distribution with zero mean and unit variance
We found that Edge Boxes have a higher recall when returning a larger number of boxes, but there is no direct relationship between the calculated score and the correctness of the detection results
Summary
The rapid development of economy has brought environmental pollution, especially for developing countries. Taken China for example, about 1 billion tons of garbage are produced every year. In this case, real time garbage detection is important for environment pollution monitoring. Supervised object detection algorithms have developed rapidly in recent years [1]–[4], they are not suitable for this task because they need abundant data for training which is not available for this task. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology and spectroscopy technology. It is called imaging spectroscopy [5]. HSI has the characteristic of ‘‘image cubes’’. HSI contains tens or even hundreds of spectral bands whose spectral resolutions are
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