Light field (LF) imaging enables new possibilities for digital imaging, such as digital refocusing, changing of focus plane, changing of viewpoint, scene-depth estimation, and 3D scene reconstruction, by capturing both spatial and angular information of light rays. However, one main problem in dealing with LF data is its sheer volume. In this context, efficient compression methods are needed for such a particular type of content. In this paper, we propose a content-based LF image-compression method with Gaussian process regression to improve the compression efficiency and accelerate the prediction procedure. First, the LF image is fed to the intra-frame codec of HEVC. In the prediction procedure, the prediction units (PUs) are classified as non-homogenous texture units, homogenous texture units, and visually flat units, based on the content property of the LF image. For each category, we design a corresponding Gaussian process regression (GPR)-based prediction method. Moreover, we propose a classification mechanism to exactly decide to which category the current PU belongs, so as to adjust the trade-off between the computational burden and the LF image coding efficiency. Experimental results demonstrate that the proposed LF image compression method is superior to several other state-of-the-art compression methods in terms of different quality metrics. Furthermore, the proposed method can also achieve a good visual quality of views rendered from decoded LF contents.

Agriculture 4.0 offers the potential to revolutionize the agriculture sector through improved productivity and efficiency. However, adopting Agriculture 4.0 requires a period of transition and effort. Satellite-Airborne sensing technologies may become an opening enabler technology of this new paradigm due to its fast deployment process and flexible infrastructure. This paper provides an overview of the technology, trends, challenges, and opportunities in agriculture and climate-resilient sensing technologies. The research covers critical enabling technologies such as Low Altitude Platforms (LAPs) (i.e., Drones, Tethered Ballon), High Altitude Platforms (HAPs) (i.e., Airships, HAPs Balloons, and Aircraft), and satellites, as well as recent advancements in data processing, and digital twins, with some examples from agricultural research projects. Furthermore, this paper explores some challenges in agriculture and the technological deployment of satellite-airborne sensing technologies. Finally, this paper provides some potential opportunities for satellite-airborne sensing technologies for agricultural purposes. This paper may become a guide for adopting Industry 4.0 by leveraging satellite-airborne network technologies.

A novel model of image segmentation based on watershed method is proposed in this paper. To prevent the oversegmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement, the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the new algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over-segmentation.

In deeper layers, ResNet heavily depends on skip connections and Relu. Although skip connections have demonstrated their usefulness in networks, a major issue arises when the dimensions between layers are not consistent. In such cases, it is necessary to use techniques such as zero-padding or projection to match the dimensions between layers. These adjustments increase the complexity of the network architecture, resulting in an increase in parameter number and a rise in computational costs. Another problem is the vanishing gradient caused by utilizing Relu. In our model, after making appropriate adjustments to the inception blocks, we replace the deeper layers of ResNet with modified inception blocks and Relu with our non-monotonic activation function (NMAF). To reduce parameter number, we use symmetric factorization and $1\times 1$ convolutions. Utilizing these two techniques contributed to reducing the parameter number by around 6 M parameters, which has helped reduce the run time by 30 s/epoch. Unlike Relu, NMAF addresses the deactivation problem of the non-positive number by activating the negative values and outputting small negative numbers instead of zero in Relu, which helped in enhancing the convergence speed and increasing the accuracy by 5%, 15%, and 5% for the non-noisy datasets, and 5%, 6%, 21% for non-noisy datasets.