Name
Title | ||
|---|---|---|
Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning |
Abstract | ||
|---|---|---|
Hyperspectral image classification has been an active topic of research. In recent years, it has been found that light detection and ranging (LiDAR) data provide a source of complementary information that can greatly assist in the classification of hyperspectral data, in particular when it is difficult to separate complex classes. This is because, in addition to the spatial and the spectral information provided by hyperspectral data, LiDAR can provide very valuable information about the height of the surveyed area that can help with the discrimination of classes and their separability. In the past, several efforts have been investigated for fusion of hyperspectral and LiDAR data, with some efforts driven by the morphological information that can be derived from both data sources. However, a main challenge for the learning approaches is how to exploit the information coming from multiple features. Specifically, it has been found that simple concatenation or stacking of features such as morphological attribute profiles (APs) may contain redundant information. In addition, a significant increase in the number of features may lead to very high-dimensional input features. This is in contrast with the limited number of training samples often available in remote-sensing applications, which may lead to the Hughes effect. In this work, we develop a new efficient strategy for fusion and classification of hyperspectral and LiDAR data. Our approach has been designed to integrate multiple types of features extracted from these data. An important characteristic of the presented approach is that it does not require any regularization parameters, so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a hyperspectral image and a LiDAR-derived digital surface model (DSM) collected over the University of Houston campus and the neighboring urban area, indicate that the proposed fram- work for multiple feature learning provides state-of-the-art classification results. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTARS.2015.2432037 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
digital surface model (dsm),hyperspectral,light detection and ranging (lidar),multiple feature learning,hyperspectral imaging,remote sensing application,feature extraction,remote sensing,laser radar,sensor fusion,image classification,data mining,support vector machines | Remote sensing,Ranging,Lidar,Artificial intelligence,Concatenation,Computer vision,Full spectral imaging,Pattern recognition,Support vector machine,Hyperspectral imaging,Feature extraction,Feature learning,Mathematics | Journal |
Volume | Issue | ISSN |
PP | 99 | 1939-1404 |
Citations | PageRank | References |
17 | 0.67 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khodadadzadeh, M. | 1 | 17 | 0.67 |
| Jun Li | 2 | 1360 | 97.59 |
| Prasad S. Thenkabail | 3 | 210 | 30.03 |
| Antonio Plaza | 4 | 3475 | 262.63 |