138 research outputs found
High-Resolution Remote Sensing Image Classification Using Associative Hierarchical CRF Considering Segmentation Quality
This letter proposes an associative hierarchical conditional random field (AHCRF) model to improve the classification accuracy of high-resolution remote sensing images. It considers segmentation quality of superpixels, avoids a time-consuming selection of optimal scale parameters, and alleviates the problem of classification accuracy sensitive to undersegmentation errors that is present in traditional object-oriented classification methods. The model is built on a graph hierarchy, including the pixel layer as a base layer and multiple superpixel layers derived from a mean shift presegmentation. It extracts clustered features of pixels for superpixels at each layer and then defines the potentials of the AHCRF model. We suggest a weighted version of the interlayer potential using the size of a superpixel as a measure to reflect segmentation quality. In this way, erroneously labeled pixels of a superpixel are penalized. Experiments are presented using a part of the downsampled Vaihingen data from the ISPRS benchmark data set. Results confirm that our model shows more than 80% overall classification accuracy and is superior to the original AHCRF model and comparable to other models. It also alleviates the choosing of suitable segmentation parameters
Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data
With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection.
This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model
Line Based Multi-Range Asymmetric Conditional Random Field For Terrestrial Laser Scanning Data Classification
Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, highly dense three-dimensional point cloud of object surfaces by laser range finding. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. However, classifying massive TLS data faces many challenges, such as complex urban scene, partial data acquisition from occlusion. To make an automatic, accurate and robust TLS data classification, we present a line-based multi-range asymmetric Conditional Random Field algorithm.
The first contribution is to propose a line-base TLS data classification method. In this thesis, we are interested in seven classes: building, roof, pedestrian road (PR), tree, low man-made object (LMO), vehicle road (VR), and low vegetation (LV). The line-based classification is implemented in each scan profile, which follows the line profiling nature of laser scanning mechanism.Ten conventional local classifiers are tested, including popular generative and discriminative classifiers, and experimental results validate that the line-based method can achieve satisfying classification performance. However, local classifiers implement labeling task on individual line independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. The second contribution is to propose a multi-range asymmetric Conditional Random Field (maCRF) model, which uses object context as post-classification to improve the performance of a local generative classifier. The maCRF incorporates appearance, local smoothness constraint, and global scene layout regularity together into a probabilistic graphical model. The local smoothness enforces that lines in a local area to have the same class label, while scene layout favours an asymmetric regularity of spatial arrangement between different object classes within long-range, which is considered both in vertical (above-bellow relation) and horizontal (front-behind) directions. The asymmetric regularity allows capturing directional spatial arrangement between pairwise objects (e.g. it allows ground is lower than building, not vice-versa). The third contribution is to extend the maCRF model by adding across scan profile context, which is called Across scan profile Multi-range Asymmetric Conditional Random Field (amaCRF) model. Due to the sweeping nature of laser scanning, the sequentially acquired TLS data has strong spatial dependency, and the across scan profile context can provide more contextual information. The final contribution is to propose a sequential classification strategy. Along the sweeping direction of laser scanning, amaCRF models were sequentially constructed. By dynamically updating posterior probability of common scan profiles, contextual information propagates through adjacent scan profiles
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
To better address challenging issues of the irregularity and inhomogeneity
inherently present in 3D point clouds, researchers have been shifting their
focus from the design of hand-craft point feature towards the learning of 3D
point signatures using deep neural networks for 3D point cloud classification.
Recent proposed deep learning based point cloud classification methods either
apply 2D CNN on projected feature images or apply 1D convolutional layers
directly on raw point sets. These methods cannot adequately recognize
fine-grained local structures caused by the uneven density distribution of the
point cloud data. In this paper, to address this challenging issue, we
introduced a density-aware convolution module which uses the point-wise density
to re-weight the learnable weights of convolution kernels. The proposed
convolution module is able to fully approximate the 3D continuous convolution
on unevenly distributed 3D point sets. Based on this convolution module, we
further developed a multi-scale fully convolutional neural network with
downsampling and upsampling blocks to enable hierarchical point feature
learning. In addition, to regularize the global semantic context, we
implemented a context encoding module to predict a global context encoding and
formulated a context encoding regularizer to enforce the predicted context
encoding to be aligned with the ground truth one. The overall network can be
trained in an end-to-end fashion with the raw 3D coordinates as well as the
height above ground as inputs. Experiments on the International Society for
Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated
the superiority of the proposed method for point cloud classification. Our
model achieved a new state-of-the-art performance with an average F1 score of
71.2% and improved the performance by a large margin on several categories
Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?
In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set
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