PSASL: Pixel-Level and Superpixel-Level Aware Subspace Learning for Hyperspectral Image Classification

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

Section: Llc-based Dictionary Learning and Sparse Coding For Single Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

Tong

Zhang

et al. 2019

“…Li et al [4] proposed a deep-learning network based on multi-level voxel features fusion for point cloud classification. The feature dimensions used by the above methods are relatively high, and are found to be carrying noise and redundant information [13]. To overcome this drawback, dimensionality reduction and sparse representation are widely used.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, if we can successfully classify a large volumes of point clouds using only a small percentage of training samples, it should have a great practical applicability because the time and labor cost will be significantly reduced [5]. To solve this problem, [13,[17][18][19] proposed semi-supervised or supervised classification methods for feature transformation matrix and classifier joint learning. For example, Mei et al [17] concatenated multiple single point features to form high-dimensional features for each point, and then used the joint constraints of margin, graph of adjacency and labels to train the model with a small portion of samples based on a semi-supervised framework.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Tong

Chen

et al. 2020

Self Cite

In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.

“…Clustering method: This method is an unsupervised learning method, which heuristically clusters points with similar attributes into the same class to meet the requirements of cost functions. Representative methods include the classic K-means algorithm [23], Euclidean distance clustering algorithm [24], mean shift clustering [25][26][27], hierarchical clustering [28][29][30], sample density-based clustering [31,32], and mixed kernel density function clustering [33]. For example, Wu et al [24] introduced a smooth threshold constraint to the traditional Euclidean clustering algorithm to prevent over-and/or under-segmentation problems.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy.