No abstract

Precision agriculture using unmanned aerial vehicles (UAVs) is gaining popularity. These UAVs provide a unique aerial perspective suitable for inspecting agricultural fields. With the use of hyperspectral cameras, complex inspection tasks are being automated. Payload constraints of UAVs require low weight and small hyperspectral cameras; however, such cameras with a multispectral color filter array suffer from crosstalk and a low spatial resolution. The research described in this paper aims to reduce crosstalk and to increase spatial resolution using convolutional neural networks. We propose a similarity maximization framework which is trained to perform end-to-end demosaicking and crosstalk-correction of a 4 × 4 raw mosaic image. The proposed method produces a hyperspectral image cube with 16 times the spatial resolution of the original cube while retaining a median structural similarity (SSIM) index of 0.85 (compared to an SSIM of 0.55 when using bilinear interpolation). Furthermore, this paper provides insight into the beneficial effects of crosstalk for hyperspectral demosaicking and gives best practices for several architectural and hyperparameter variations as well as a theoretical reasoning behind certain observations.

Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Given the hyper-spectral imaging unique potentials in grasping the polymer characteristics of different materials, it is commonly used in sorting procedures. In a practical plastic sorting scenario, multiple plastic flakes may overlap which depending on their characteristics, the overlap can be reflected in their spectral signature. In this work, we use hyper-spectral imaging for the segmentation of three types of plastic flakes and their possible overlapping combinations. We propose an intuitive and simple multi-label encoding approach, bitfield encoding, to account for the overlapping regions. With our experiments, we show that the bitfield encoding improves over the baseline single-label approach and we further demonstrate its potential in predicting multiple labels for overlapping classes even when the model is only trained with non-overlapping classes.

No abstract

The importance of plastic waste recycling is undeniable. In this respect, computer vision and deep learning enable solutions through the automated analysis of short-wave-infrared hyper-spectral images of plastics. In this paper, we offer an exhaustive empirical study to show the importance of efficient model selection for resolving the task of hyper-spectral image segmentation of various plastic flakes using deep learning. We assess the complexity level of generic and specialized models and infer their performance capacity: generic models are often unnecessarily complex. We introduce two variants of a specialized hyper-spectral architecture, PlasticNet, that outperforms several well-known segmentation architectures in both performance as well as computational complexity. In addition, we shed lights on the significance of signal pre-processing within the realm of hyper-spectral imaging. To complete our contribution, we introduce the largest, most versatile hyper-spectral dataset of plastic flakes of four primary polymer types.

No abstract

Given the hyper-spectral imaging unique potentials in grasping the polymer characteristics of different materials, it is commonly used in sorting procedures. In a practical plastic sorting scenario, multiple plastic flakes may overlap which depending on their characteristics, the overlap can be reflected in their spectral signature. In this work, we use hyper-spectral imaging for the segmentation of three types of plastic flakes and their possible overlapping combinations. We propose an intuitive and simple multi-label encoding approach, bitfield encoding, to account for the overlapping regions. With our experiments, we show that the bitfield encoding improves over the baseline single-label approach and we further demonstrate its potential in predicting multiple labels for overlapping classes even when the model is only trained with non-overlapping classes.