Mars Reconnaissance Orbiter's Mars Color Imager (MARCI): A New Workflow for Processing Its Image Data

The Mars Reconnaissance Orbiter's (MRO's) Mars Color Imager (MARCI) has returned approximately daily, approximately global image data of Mars since late 2006, in up to seven different colors, from ultraviolet through near-infrared. To-date, that is over 5300 Mars days of data, nearly eight full Mars Years, or more than 15 Earth years. The data are taken at up to nearly 500 meters per pixel, and the nearly circular orbit of MRO and its consistent early afternoon imaging provide an unprecedented baseline of data with which to study Mars' atmosphere and surface processes. Unfortunately, processing MARCI data is difficult, fraught with exploding file sizes, issues that require workarounds in free software, and other problems that make this a severely under-utilized dataset. This paper discusses a workflow to process MARCI data to their fullest, including suggestions on how to work around issues unique to MARCI and how the data work with the current version of the free software ISIS (Integrated Software for Imagers and Spectrometers). Discussion of some trades that can be made to dramatically speed data processing are also described. Examples of processed MARCI images, mosaics, and color composites are shown, demonstrating the abilities of this workflow on global, regional, and local areas at the full, 96 pixels per degree scale afforded by MARCI

Utilization of convolutional neural networks in the classification of snowflakes based on images by a multi-angle snowflake camera

2019 Summer.Includes bibliographical references.Recent developments in machine learning are applied to in-situ data collected by a Multi-Angle Snowflake Camera (MASC), incorporating convolutional and residual networks in big data environments. These networks provide the following benefits: require little initial preparation and automatic feature extraction, high accuracy and through transfer learning techniques, and relatively small training sets. The networks have large supporting communities and are popular for image processing and classification tasks specifically. In this paper, a convolutional neural network (CNN) is adapted and tasked with classifying images captured from two storm events in December 2014 and February 2015 in Greeley, Colorado. A training data set containing 1400 MASC images was developed by visual inspection of recognizable snowflake geometries and sorted into six distinct classes. The network trained on this data set achieved a mean accuracy of 93.4% and displayed excellent generality. A separate training data set was developed sorting flakes into three classes showcasing distinct degrees of riming. The network was then tasked with classifying images and estimating where flakes fell within this riming scale. The riming degree estimator yields promising initial results but would benefit from larger training sets. Future applications are discussed

APPLICATION OF INTELLIGENT SYSTEM WITH BACKPROPAGATION MODEL IN CLOUD IMAGE CLASSIFICATION

The clouds have different patterns on each type and each type has different properties. The introduction of the type, shape, and nature of the cloud is indispensable in the weather forecasts so that the clouds can be classified. There are several methods used in the image classification process that is the method of the artificial neural network Backpropagation. The method of Backpropagation is one of the methods used for the classification process, in this research Backpropagation used on the training and testing process for the introduction of cloud imagery aimed at determining the type of cloud, before the second These stages are carried out imagery through the preprocessing process. From the training conducted using the Backpropagation method shows that this method generates the best weight value and saves that value into the database to do the testing process using a neural network Backpropagation. In addition, Backpropagation also has the ability to reduce errors by continuously correcting the weight until reaching the maximum target. Data used for training data as many as 92 cloud type image with each type of 10 imagery. In this study obtained a system success rate of 60.6%

Cloud Tracking Winds on Mars using EMM-EXI imaging

The Martian atmospheric wind structure is a major unknown in our understanding of Mars’ climate because it is difficult to measure wind remotely. The Emirates eXploration Imager (EXI) instrument on board the Emirates Mars Mission (EMM) takes visible and ultraviolet images of the whole hemisphere of the planet at a time, and can capture complete weather systems at once, along with their evolution over time. This project uses EXI 320 nm observations to measure winds on Mars using Correlation Imaging Velocimetry (CIV), a cloud tracking method based on software developed for laboratory fluid dynamics experiments, and with significant heritage in planetary imaging. We focused on observation sequences designed specifically to capture high- cadence imaging of Mars, with images separated by as little as five minutes. The experimental procedure was first to look for overlapping pairs of images that contain trackable features, but with an image separation large enough in time to see these features move, and then create cropped pairs of processed images projected on a 0.05° × 0.05° degree longitude-latitude grid. We then optimized the CIV parameters for a representative pair of images, and finally applied the CIV method to as many EXI image pairs as possible. The results were presented as wind field maps, latitudinal profiles, zonal and meridional analysis plots and were compared to the output of an observationally calibrated atmospheric general circulation model, the Mars Climate Database (MCD)

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

Physics methods for image classification with Deep Neural Networks

The studies performed in this thesis see their light in the context of an internship carried out in Porini, a dynamic business versed in digital consulting and software development. The ultimate goal of this research is to develop an algorithm to perform product recognition of common items found in supermarkets or grocery shops. The first part of the analysis will consider a simplified toy model, in order to gain a deeper insight on the data at disposal. In particular, a manual feature extraction will be designed, consisting of an equalisation procedure and a custom-built cropping for the images. A novel classification model will be then defined using average RGB histograms as references for each product class and testing out different metrics to quantify the similarity between two images. This implementation will culminate in the realization of a proof of concept in the form of an application for mobile platforms. In the second part of the study, object detection and recognition will be tackled in a more generalized context. This will require the employment of more advanced, pre-built algorithms, particularly in the form of deep convolutional neural networks. Specifically, a focus will be made on the single-shot approach, where a duly trained detector only observes the image at once, as a whole, before outputting its detection prediction; an exploratory analysis will be performed taking advantage of the YOLO model, a state-of-the-art implementation in the field. The results obtained are very satisfactory: the first part of the study has led to the definition of a new customized algorithm for classification which is robust and well-optimized, while in the second one promising foundations have been laid in the development of advanced object recognition tools for general use cases.ope

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

Soil endowments, production technologies and missing women in India

The female population deficit in India has been explained in a number of ways, but the great heterogeneity in the deficit across districts within India still remains an open question. This paper argues that across India, a largely agrarian economy, soil texture varies exogenously and determines the workability of the soil and the technology used in land preparation. Deep tillage, possible only in lighter and looser loamy soils, reduces the use of labor in cultivation tasks performed by women and has a negative impact on the relative value of girls to a household. The analysis finds that soil texture explains a large part of the variation in women's relative participation in agriculture and in infant sex ratios across districts in India.Labor Markets,Common Property Resource Development,Population Policies,Crops&Crop Management Systems,Labor Policies

Simplified and Advanced Sentinel-2-Based Precision Nitrogen Management of Wheat

This study compares simplified and advanced precision nitrogen (N) fertilization approaches for winter wheat relying on Sentinel-2 NDVI, grain yield maps, and protein content. Five N fertilization treatments were compared: (1) a standard rate, calculated by a typical N balance (Flat-N); (2) a variable rate calculated using a simplified linear model, adopting a proportional strategy (NDVI directly related) (Var-N-dir); (3) a variable rate calculated using a simplified linear model, adopting a compensative strategy (NDVI inversely related) (Var-N-inv); (4) a variable rate calculated using the AgroSat model (Var-N-Agrosat); and (5) a variable rate calculated applying the Agricolus model (Var-N-Agricolus). The study was carried out in four fields over two cropping seasons with a randomized blocks design. Results indicate that the weather remains the main factor influencing yield, as it typically happens in a rainfed crop. No substantial differences in crop yield were observed among the N fertilization models within each year and experimental location. However, in the more favorable season, the low-input direct model (Var-N-dir) resulted as the best choice, providing the higher NUE (nitrogen use efficiency) value. In the less favorable season, results showed a better performance of the advanced models (Var-N-Agricolus and Var-N-Agrosat), which limited yield losses and reduced intra-field variability, with relevant importance given to the increasing frequency of abnormal climate phenomena. In general, all these VRT approaches allowed reduction of the excess of fertilizers, preservation of the environment, and saving money

Infiltration and surface runoff dynamics on dryland hillslopes: a new method

Drylands cover approximately 41% of the Earth’s land surface (Middleton and Thomas, 1997); a habitat for over 38% of the planet's population (Huang et al., 2017). Understanding the interaction between ground surface characteristics, infiltration and overland flow in this environment is paramount to identifying areas vulnerable to erosion and flash flooding. Currently, infiltration is measured in drylands using techniques which are often not suited to the environment. Existing measurement methods typically cannot be used on steep slopes, and slopes with stone or vegetation cover, without disturbing the natural soil. As well as this, the impact of overland flow is often neglected from measurements. Here, a new method for quantifying infiltration and overland flow is presented: ‘the infiltrator’. The device outputs a pulse of water to the surface, allowing the measurement of runoff dimensions. Soil surface and slope characteristics are also measured with the use of field and GIS based techniques. The methods enable two main research questions to be assessed: (i) the impact of surface cover on surface runoff, and (ii) the influence of surface characteristics on flow concentration. The infiltrator was used successfully on rangeland slopes in a semi-arid environment (Salema, Western Algarve, Portugal), allowing for assessment of infiltration and overland flow, without disturbing the natural soil. Using regression modelling, the results from experimentation using the infiltrator indicted that: (i) infiltration and the nature of surface runoff are strongly related to stone and vegetation cover, and (ii) flow concentration controls include those identified in (i), as well as surface roughness and slope angle. The new method effectively enables the quantification of infiltration and overland flow, whilst remaining representative of the surface. It can be used on slopes up to 40°, and is an inexpensive, quick solution to characterising the vulnerability of dryland slopes to surface runoff and erosion