Open source R for applying machine learning to RPAS remote sensing images

The increase in the number of remote sensing platforms, ranging from satellites to close-range Remotely Piloted Aircraft System (RPAS), is leading to a growing demand for new image processing and classification tools. This article presents a comparison of the Random Forest (RF) and Support Vector Machine (SVM) machine-learning algorithms for extracting land-use classes in RPAS-derived orthomosaic using open source R packages. The camera used in this work captures the reflectance of the Red, Blue, Green and Near Infrared channels of a target. The full dataset is therefore a 4-channel raster image. The classification performance of the two methods is tested at varying sizes of training sets. The SVM and RF are evaluated using Kappa index, classification accuracy and classification error as accuracy metrics. The training sets are randomly obtained as subset of 2 to 20% of the total number of raster cells, with stratified sampling according to the land-use classes. Ten runs are done for each training set to calculate the variance in results. The control dataset consists of an independent classification obtained by photointerpretation. The validation is carried out(i) using the K-Fold cross validation, (ii) using the pixels from the validation test set, and (iii) using the pixels from the full test set. Validation with K-fold and with the validation dataset show SVM give better results, but RF prove to be more performing when training size is larger. Classification error and classification accuracy follow the trend of Kappa index

Моніторинг об’єктів теплоенергетики з використанням безпілотних літальних апаратів

Розглянуто засади створення і практичного використання мобільних інформаційно-вимірювальних систем моніторингу характеристик довкілля об’єктів теплоенергетики та можливості використання з цією метою безпілотних літальних апаратів.Рассмотрены основы создания и практического использования мобильных информационно-измерительных систем мониторинга объектов теплоэнергетики и возможность использования с этой целью беспилотных летательных аппаратов.Considered principles of creation and practical use of mobile information and measurement systems to monitor environmental characteristics of thermal power facilities and the possibility of using this purpose unmanned aerial vehicles

A deep learning application to map weed spatial extent from unmanned aerial vehicles imagery

Weed infestation is a global threat to agricultural productivity, leading to low yields and financial losses. Weed detection, based on applying machine learning to imagery collected by Unmanned Aerial Vehicles (UAV) has shown potential in the past; however, validation on large data-sets (e.g., across a wide number of different fields) remains lacking, with few solutions actually made operational. Here, we demonstrate the feasibility of automatically detecting weeds in winter wheat fields based on deep learning methods applied to UAV data at scale. Focusing on black-grass (the most pernicious weed across northwest Europe), we show high performance (i.e., accuracy above 0.9) and highly statistically significant correlation (i.e., ro > 0.75 and p < 0.00001) between imagery-derived local and global weed maps and out-of-bag field survey data, collected by experts over 31 fields (205 hectares) in the UK. We demonstrate how the developed deep learning model can be made available via an easy-to-use docker container, with results accessible through an interactive dashboard. Using this approach, clickable weed maps can be created and deployed rapidly, allowing the user to explore actual model predictions for each field. This shows the potential for this approach to be used operationally and influence agronomic decision-making in the real world

RC Hovercraft: An I-Bylogical Enzyme (I-BE) Biosensor Carrier

In this research, a hovercraft was developed as a transportation tool to carry an I-Bylogical Enzyme (I-BE) biosensor. The main function of the biosensor is to measure the level of dangerous chemical materials from factory’s liquid waste. Hence, fiber and acrylic materials, that widely known for its resistance to chemical reaction, are needed to build the hovercraft. By using Atmega 8535 microcontroller and Arduino Uno board, a remote control was choosen to navigate hovercraft’s movement with support of 6 channels Transmitter Receiver. As a result, after series of tests that were carried out, indicate that the hovercraft’s prototype was found to meet design and requirements expectation

A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAV

A new physically based method to estimate hemispheric-directional reflectance factor (HDRF) from lightweight multispectral cameras that have a downwelling irradiance sensor is presented. It combines radiometry with photogrammetric computer vision to derive geometrically and radiometrically accurate data purely from the images, without requiring reflectance targets or any other additional information apart from the imagery. The sky sensor orientation is initially computed using photogrammetric computer vision and revised with a non-linear regression comprising radiometric and photogrammetry-derived information. It works for both clear sky and overcast conditions. A ground-based test acquisition of a Spectralon target observed from different viewing directions and with different sun positions using a typical multispectral sensor configuration for clear sky and overcast showed that both the overall value and the directionality of the reflectance factor as reported in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5% for overcast sky was observed

Parallel processing applied to image mosaic generation

The automatic construction of large mosaics obtained from high resolution digital images is an area of great importance, with applications in different areas. In agriculture, the requirements of cartographic accuracy of mosaics of annual or perennial crops are not so high, but the speed in obtaining them is the most critical factor. The efficiency in decision making is related to the obtaining faster and more accurate information, especially in the control of pests, diseases or fire control. This project proposes a methodology based on SIFT Transform and parallel processing to build mosaics automatically, using high resolution agricultural aerial images. Build mosaics with high resolution images requires high computational effort for processing them. To treat the problem of computational effort, the standard OpenMP of parallel processing was used to accelerate the process and results are presented for a computer with 2, 4 and 8 threads