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The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral band that can improve the performance of computer vision algorithms for solving remote sensing tasks. We study the use of a generative adversarial network (GAN) approach in the task of the NIR band generation using only RGB channels of high-resolution satellite imagery. We evaluate the impact of a generated channel on the model performance to solve the forest segmentation task. Our results show an increase in model accuracy when using generated NIR compared to the baseline model, which uses only RGB ( 0.947 and 0.914 F1-scores, respectively). The presented study shows the advantages of generating the extra band such as the opportunity to reduce the required amount of labeled data. [ABSTRACT FROM AUTHOR]

Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects of interest on a global scale. Machine learning and computer vision (CV) approaches show themselves as promising tools for automatizing satellite image analysis. However, there are limitations in using CV for satellite data. Mainly, the crucial one is the amount of data available for model training. This paper presents a novel image augmentation approach called MixChannel that helps to address this limitation and improve the accuracy of solving segmentation and classification tasks with multispectral satellite images. The core idea is to utilize the fact that there is usually more than one image for each location in remote sensing tasks, and this extra data can be mixed to achieve the more robust performance of the trained models. The proposed approach substitutes some channels of the original training image with channels from other images of the exact location to mix auxiliary data. This augmentation technique preserves the spatial features of the original image and adds natural color variability with some probability. We also show an efficient algorithm to tune channel substitution probabilities. We report that the MixChannel image augmentation method provides a noticeable increase in performance of all the considered models in the studied forest types classification problem. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning-based apple tree health assessment system using image segmentation. • End-to-end implementation with a user-friendly interface. • The developed system was tested under field conditions in an apple orchard. • The platform allows to speed up the digital phenotyping process. Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution. [ABSTRACT FROM AUTHOR]