In this paper, we propose an analysis on the combinative effect of high-resolution airborne image and light
detection and ranging (LIDAR) data for the classification of complex urban areas. In greater detail, the proposed
system is composed of three models briefly. Model one includes an advanced kernelized fuzzy c-means
classification method for high-resolution airborne image. The characteristics of LIDAR point cloud are introduced in
model two, membership degree function of buildings, vegetations and naked land have been built. In model three,
high-resolution image and elevation data form LIDAR point cloud are jointed. Experiment carried out on a complex
urban area provide interesting conclusions on the effectiveness and protentialities of the joint use of high-resolution
image and LIDAR data. In particular, the elevation data was very effective for the separation of species with similar
spectral signatures but different elevation information. Experimental results approve that elevation data can improve
classification accuracy in building occupied area obviously.
SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression
problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is
proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image
by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is
proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is
carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that
our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the
accuracy and speed of classification are improved greatly.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.