Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC–MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm’s results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC–MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC–MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC–MRF-cluster showed good stability.
In the paper, precise geometric correction of Landsat-8 images based on Kalman filter with ground control points (GCPs)
is described. The matching pixels, GCPs and systematic correction image are integrated to estimate the errors of the
position, velocity and attitude of the Landsat-8 satellite. Kalman filter was used for the optimal solution. Experiments
demonstrate that comparable accuracy could be reached applying Kalman filter in the purpose of precision mapping
using fewer GCPs when comparing to the least-square iteration method.
This paper proposes an edge-constrained Markov random field (EC-MRF) method for accurate land cover classification over urban areas using hyperspectral image and LiDAR data. EC-MRF adopts a probabilistic support vector machine for pixel-wise classification of hyperspectral and LiDAR data, while MRF performs as a postprocessing regularizer for spatial smoothness. LiDAR data improve both pixel-wise classification and postprocessing result during an EC-MRF procedure. A variable weighting coefficient, constrained by a combined edge extracted from both hyperspectral and LiDAR data, is introduced for the MRF regularizer to avoid oversmoothness and to preserve class boundaries. The EC-MRF approach is evaluated using synthetic and real data, and results indicate that it is more effective than four similar advanced methods for the classification of hyperspectral and LiDAR data.
Optical remotely sensed data, especially hyperspectral data have emerged as the most useful data source for regional
crop classification. Hyperspectral data contain fine spectra, however, their spatial coverage are narrow. Multispectral data
may not realize unique identification of crop endmembers because of coarse spectral resolution, but they do provide
broad spatial coverage. This paper proposed a method of multisensor analysis to fully make use of the virtues from both
data and to improve multispectral classification with the multispectral signatures convert from hyperspectral signatures
in overlap regions. Full-scene crop mapping using multispectral data was implemented by the multispectral signatures
and SVM classification. The accuracy assessment showed the proposed classification method is promising.
Markov random field (MRF) provides a useful model for integrating contextual information into remote sensing image classification. However, there are two limitations when using the conventional MRF model in hyperspectral image classification. First, the maximum likelihood classifier used in MRF to estimate the spectral-based probability needs accurate estimation of covariance matrix for each class, which is often hard to obtain with a small number of training samples for hyperspectral imagery. Second, a fixed spatial neighboring impact parameter for all pixels causes overcorrection of spatially high variation areas and makes class boundaries blurred. This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery. An adaptive spatial neighboring impact parameter is assigned to each pixel according to its spatial contextual correlation. Experimental results of a hyperspectral image show that the classification accuracy from the proposed method has been improved compared to those from the conventional MRF model and pixel-wise classifiers including the maximum likelihood classifier and SVM classifier.
Recently, JPEG High Dynamic Range (JPEG-HDR) remote sensing images have become popular in photogrammetry and remote sensing for its high brightness level. But how to reproduce and visualize such images in the standard display device becomes a question, a new tone mapping method in this paper is proposed to realize the visualization of the HDR remote sensing image. The experimental results presented in the paper demonstrate the fast and effective of our method.
In traditional image segmentation models based on single level set function, only two regions can be identified because
different regions are identified by the signs of single level set function. Though several segmentation models with
multiple regions have been proposed, but the largest number of regions that can be identified was limited by the number
of embedded level set functions in them. Moreover, the more embedded level set functions, the higher the time cost,
usually increasing linearly with the increase of embedded level set functions. In this paper, by introducing the
segmentation-measure function, a new model for multi-regions image segmentation based on single level set function is
proposed. At the same time, a new initialization function for the level set function is also proposed in order to reduce the
time cost of the segmentation model. The experiment results show that the new Image segmentation mode with multiple
regions proposed in this paper performs well and dramatically reduces the time cost compared with the popular model
for multiple regions proposed by Vese and Chan.