1,597 research outputs found
Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery
Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages
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
Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data
High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A geographic object-based image analysis (GEOBIA) approach was used. The data comprised National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters. Stratified-statistical-based training sampling methods were found to generate higher classification accuracies than deliberative-based sampling. Subset-based sampling, in which training data is collected from a small geographic subset area within the study site, did not notably decrease the classification accuracy. For the five machine learning algorithms investigated, support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), and learning vector quantization (LVQ), increasing the size of the training set typically improved the overall accuracy of the classification. However, RF was consistently more accurate than the other four machine learning algorithms, even when trained from a relatively small training sample set. Recursive feature elimination (RFE), which can be used to reduce the dimensionality of a training set, was found to increase the overall accuracy of both SVM and NEU classification, however the improvement in overall accuracy diminished as sample size increased. RFE resulted in only a small improvement the overall accuracy of RF classification, indicating that RF is generally insensitive to the Hughes Phenomenon. Nevertheless, as feature selection is an optional step in the classification process, and can be discarded if it has a negative effect on classification accuracy, it should be investigated as part of best practice for supervised machine land-cover classification using remotely sensed data
An Approach for Combining Airborne LiDAR and High-Resolution Aerial Color Imagery using Gaussian Processes
Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method
Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale
The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data
DETECTION OF ROOF BOUNDARIES USING LIDAR DATA AND AERIAL PHOTOGRAPHY
The recent growth in inexpensive laser scanning sensors has created entire fields of research aimed at processing this data. One application is determining the polygonal boundaries of roofs, as seen from an overhead view. The resulting building outlines have many commercial as well as military applications. My work in this area has created a segmentation algorithm where the descriptive features are computationally and theoretically simpler than previous methods. A support vector machine is used to segment data points using these features, and their use is not common for roof detection to date. Despite the simplicity of the feature calculations, the accuracy of our algorithm is similar to previous work. I also describe a basic polygonal extraction method, which is acceptable for basic roofs
Volume Component Analysis for Classification of LiDAR Data
One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach.
This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block.
This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%
Neural Network Memory Architectures for Autonomous Robot Navigation
This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics
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