C.M. Gevaert

Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.

Optimizing Multiple Kernel Learning for the Classification of UAV Data

Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL) for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM). A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

Using UAVs for map creation and updating. A case study in Rwanda

Aerial or satellite images are conventionally used for geospatial data collection. However, unmanned aerial vehicles (UAVs) are emerging as a suitable technology for providing very high spatial and temporal resolution data at a low cost. This paper aims to show the potential of using UAVs for map creation and updating. The whole workflow is introduced in the paper, using a case study in Rwanda, where 954 images were collected with a DJI Phantom 2 Vision Plus quadcopter. An orthophoto covering 0.095 km2 with a spatial resolution of 3.3 cm was produced and used to extract features with a sub-decimetre accuracy. Quantitative and qualitative control of the UAV data products were performed, indicating that the obtained accuracies comply to international standards. Moreover, possible problems and further perspectives were also discussed. The results demonstrate that UAVs provide promising opportunities to create high-resolution and highly accurate orthophotos, thus facilitating map creation and updating.

A deep learning approach to the classification of sub-decimetre resolution aerial images

Spatial-contextual features play a vital role in the classification of very high resolution aerial images characterized by sub-decimetre resolution. However, manually extracting relevant contextual features is difficult and time-consuming in the analysis of sub-decimetre resolution images, where the objects of interest are significantly larger than the pixel size. Deep learning methods allow us to replace hand-crafted features by automatically learning contextual features from the image. In this paper, we investigate the use of convolutional neural networks (CNN) for the classification of urban areas using high resolution airborne images. We also analyse the sensitivity of network hyperparameters providing an interpretation of their effect on the extraction of spatial-contextual features. Experimental results show the effectiveness of CNN in learning discriminative contextual features leading to accurate classified maps and outperforming traditional classification methods based on the extraction of textural features.

Integration of 2D and 3D features from UAV imagery for informal settlement classification using Multiple Kernel Learning

Informal settlement upgrading projects require high-resolution and up-to-date thematic maps in order to plan and design effective interventions. To this end, Unmanned Aerial Vehicles (UAVs) provide the opportunity to obtain very high resolution 2D orthomosaics and 3D point clouds where and when needed. The heterogeneous, dense structures which typically make up an informal settlement motivate the importance of integrating complex 2D and 3D features obtained from UAV data into a single classification problem. Multiple Kernel Learning (MKL) Support Vector Machines (SVMs) maintain the distinct characteristics of the different feature spaces by optimizing individual kernels for specific feature groups which are later combined into a single kernel used for classification. Both the kernel parameters and kernel weights can be optimized by considering the alignment between the kernel and an ideal kernel which would perfectly classify the samples. This paper demonstrates how extracting high-level features from both the 2D orthomosaic as well as the 3D point cloud (obtained by an UAV), and integrating them through a MKL approach, can obtain an Overall Accuracy of 90.29%, a 4% increase over the results obtained using single kernel methods.

Evaluating the Societal Impact of Using Drones to Support Urban Upgrading Projects

Unmanned Aerial Vehicles (UAVs), or drones, have been gaining enormous popularity for many applications including informal settlement upgrading. Although UAVs can be used to efficiently collect highly detailed geospatial information, there are concerns regarding the ethical implications of its usage and the potential misuse of data. The aim of this study is therefore to evaluate the societal impacts of using UAVs for informal settlement mapping through two case studies in Eastern Africa. We discuss how the geospatial information they provide is beneficial from a technical perspective and analyze how the use of UAVs can be aligned with the values of: participation, empowerment, accountability, transparency, and equity. The local concept of privacy is investigated by asking citizens of the informal settlements to identify objects appearing in UAV images which they consider to be sensitive or private. As such, our research is an explicit example of how to increase citizen participation in the discussion of geospatial data security and privacy issues over urban areas and provides a framework of strategies illustrating how such issues can be addressed.

Context-Based Filtering of Noisy Labels for Automatic Basemap Updating From UAV Data

Unmanned aerial vehicles (UAVs) have the potential to obtain high-resolution aerial imagery at frequent intervals, making them a valuable tool for urban planners who require up-to-date basemaps. Supervised classification methods can be exploited to translate the UAV data into such basemaps. However, these methods require labeled training samples, the collection of which may be complex and time consuming. Existing spatial datasets can be exploited to provide the training labels, but these often contain errors due to differences in the date or resolution of the dataset from which these outdated labels were obtained. In this paper, we propose an approach for updating basemaps using global and local contextual cues to automatically remove unreliable samples from the training set, and thereby, improve the classification accuracy. Using UAV datasets over Kigali, Rwanda, and Dar es Salaam, Tanzania, we demonstrate how the amount of mislabeled training samples can be reduced by 44.1% and 35.5%, respectively, leading to a classification accuracy of 92.1% in Kigali and 91.3% in Dar es Salaam. To achieve the same accuracy in Dar es Salaam, between 50000 and 60000 manually labeled image segments would be needed. This demonstrates that the proposed approach of using outdated spatial data to provide labels and iteratively removing unreliable samples is a viable method for obtaining high classification accuracies while reducing the costly step of acquiring labeled training samples.

An automated technique for basemap updating using UAV data

The increased reliance on geospatial data for decision-making in urban planning makes it imperative that the available spatial information is up-to-date and faithfully represents reality. This calls for map updating methods which support the integration of data from different sources in an automated manner. In this paper, we utilize existing basemap information to provide the initial data labels, thus reducing the lengthy process of label acquisition. However, we take into account that a portion of these labels are likely to be incorrect due to changes such as new constructions. We then cast the updating problem as a supervised classification with noisy training labels. Through an iterative approach, training samples which rank low on two criteria (label consistency and contextual consistency) are considered to be unreliable and removed from the training set. This technique is demonstrated in the specific context in which data obtained from an Unmanned Aerial Vehicle (UAV) is used to update building outlines in an informal settlement in Kigali, Rwanda. The proposed approach is able to accurately classify 95.34% of the UAV imagery even though the original labels are based on data obtained from outdated aerial imagery of a lower spatial resolution, causing 14.3% of the segments to have an incorrect training label. In this paper, we describe the proposed method, demonstrate the importance of both the contextual consistency and label consistency for filtering the training samples, discuss the robustness of the method to noise levels, and discuss the implications of this approach for other applications.