Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study
In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeCPUGeneral ClassificationLand Cover ClassificationSimilar Papers 제목 키워드 기반
LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification
The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection me…
image-classificationImage ClassificationHSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion
The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications. While previous studies have explored th…
ClassificationA graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery
Recognising individual trees within remotely sensed imagery has important applications in forest ecology and management. Several algorithms for tree delineation have been suggested, mostly based on locating local maxima …
Computational EfficiencyManagementDynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification
Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task. Existing multi-source remote sensing data classification methods often rely on human-designed frameworks for feature extraction, which …
ClassificationToulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques
Airborne hyperspectral images can be used to map the land cover in large urban areas, thanks to their very high spatial and spectral resolutions on a wide spectral domain. While the spectral dimension of hyperspectral im…
Representation LearningSelf-Supervised Learning