Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique

Abstract

Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%.

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

“…Therefore, it is necessary to develop near infrared detectors with wide band and high resolution. (3) In terms of reconnaissance methods, it can be seen from the experiment that a single reconnaissance method cannot meet the needs of war. Therefore, multi-band hyperspectral reconnaissance, infrared reconnaissance, radar detection and reconnaissance and other reconnaissance methods should be organically combined to achieve the goal of detecting various types of camouflage.…”

“…Imaging spectrum technology, also known as hyperspectral imaging technology, is a new technology that integrates imaging technology and spectral analysis technology. It is widely used in agriculture [1], mineral detection [2], environmental detection [3] and other fields. Hyperspectral images can break the restriction of two-dimensional image space, expand object information to the spectral dimension, and effectively improve the accuracy and precision of target classification and detection.…”

Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification

“…It is often claimed that PCA is better for data compression purposes but is not suitable for extracting the most informative feature in the classification task [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The reasons for this are: (i) PCA may not catch the detailed local statistics, as it determines the overall characteristics of the entire HSI; (ii) the top principal components (PCs) or transformed features may not always contain the informative structure of the entire HSI (i.e., the tasks are biased in PCs with high variance); and (iii) PCA requires a high computational cost for high-volume hyperspectral data, as it considers the global statistics [ 30 , 31 , 32 ].…”

Intelligent Identification of Early Esophageal Cancer by Band-Selective Hyperspectral Imaging

“…HSI has previously been used in numerous classifications fields, such as agriculture [ 33 ], astronomy [ 34 ], military [ 35 ], biosensors [ 36 ], air pollution detection [ 37 , 38 ], remote sensing [ 39 ], dental imaging [ 40 ], environment monitoring [ 41 ], satellite photography [ 42 ], cancer detection [ 43 ], forestry monitoring [ 44 ], food security [ 45 ], natural resource surveying [ 46 ], vegetation observation [ 47 ], and geological mapping [ 48 ]. The advantage of HSI lies in its excellent resolution, and with a minimum spectral resolution of less than 10 nm, it can be used to obtain more information than RGB images [ 49 ].…”