Abstract
Precise and timely classification of land cover types plays an important role in land resources planning and management. In this paper, nine kinds of land cover types in the acquired hyperspectral scene are classified based on the kernel collaborative representation method. To reduce the spectral shift caused by adjacency effect when mining the spatial-spectral features, a correlation coefficient-weighted spatial filtering operation is proposed in this paper. Additionally, by introducing this operation into the kernel collaborative representation method with Tikhonov regularization (KCRT) and discriminative KCRT (DKCRT) method, respectively, the weighted spatial-spectral KCRT (WSSKCRT) and weighted spatial-spectral DKCRT (WSSDKCRT) methods are constructed for land cover classification. Furthermore, aiming at the problem of difficulty of pixel labeling in hyperspectral images, this paper attempts to establish an effective land cover classification model in the case of small-size labeled samples. The proposed WSSKCRT and WSSDKCRT methods are compared with four methods, i.e., KCRT, DKCRT, KCRT with composite kernel (KCRT-CK), and joint DKCRT (JDKCRT). The experimental results show that the proposed WSSKCRT method achieves the best classification performance, and WSSKCRT and WSSDKCRT outperform KCRT-CK and JDKCRT, respectively, obtaining the OA over 94% with only 540 labeled training samples, which indicates that the proposed weighted spatial filtering operation can effectively alleviate the spectral shift caused by adjacency effect, and it can effectively classify land cover types under the situation of small-size labeled samples.
Highlights
The accurate classification of land cover types is the key and important foundation for land cover mapping
joint DKCRT (JDKCRT), respectively, whichwhich indicates that the proposed weighted spatial spatial filteringfiltering operaoperation can effectively alleviate the spectral shift caused by adjacency effect mining tion can effectively alleviate the spectral shift caused by adjacency effect whenwhen mining the spatial-spectral features of hyperspectral images
The proposed weighted spatial-spectral KCRT (WSSKCRT) and WSSDKCRT methods achieve the promising classification performance with the overall accuracy (OA) over 94%, which indicates that the proposed methods can effectively classify land cover types under the situation of small-size labeled samples
Summary
The accurate classification of land cover types is the key and important foundation for land cover mapping. And timely updating land cover mapping information can provide important theoretical basis for decision-making of land resource planning and management, environmental protection, precision agriculture, landscape pattern analysis, and so on [1,2,3]. The traditional land cover information collection method based on field survey can provide accurate land cover details, it costs a lot of manpower and time, and it cannot be carried out under some environmental conditions [1]. Remote sensing technology has become one of the most important means of land cover mapping, because it can efficiently and contactlessly obtain ground object information on a large scale [4,5]. In the past few years, researchers have utilized various remote sensing data to classify and map land cover types and achieved satisfactory results, such as satellite or airborne RGB images [6,7], multispectral images [8,9], hyperspectral images [10,11], synthetic aperture radar [12,13] and multi-source remote sensing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
