The rapid, accurate, and automated extraction of surface water is highly important for conducting reliable and necessary surface water monitoring endeavors. Classification methods commonly exhibit high precision but also have a low degree of automation or narrow scope of application; commonly used water index methods are highly efficient, but they easily mistake other targets with similar spectral characteristics for surface water. Simultaneously achieving precision, efficiency, and automation within a single method is a challenge. To address these problems, we simplify the normalized different water index (NDWI) to a band ratio index and traverse the neighborhood of the extreme in the histogram to determine two peaks and one trough between the peaks in the two-mode method, and we then compare the middle value of the two peaks with the value of the trough to confirm the threshold of the surface water. We use the modified two-mode method to extract Poyang Lake from four Chinese Gaofen (GF)-1 remote sensing images corresponding to different seasons, and then compare the results with those obtained by the NDWI index and the maximization of interclass variance (OTSU) method. The comparison shows that our method has higher and more stable accuracy, especially during the drought period for Poyang Lake. However, polluted water, narrow rivers, bridges, and residential areas along the lake are sometimes mistakenly extracted. Finally, the advantages and prospects of the proposed method are discussed.
The commercialization of high resolution remote sensing image provides the image application more widely space. How
to extract the interested important objects quickly and exactly from remote sensing image is always the research focus.
After analyzed the characteristics of road in high resolution image, the paper constructed the road extraction model based
on MRF and Bayesian. And finally the validity of the method was confirmed by an example.
Projection Pursuit is applied to explore the potential structures and characters of the multi-dimension data through projecting the high dimensional data set into a low dimensional data space while retaining the information of interest. For the application of hyperspectral images analysis, the detection of small man-made objects is very difficult. But the man-made object can be viewed as anomalies in an unknown environment due to the fact that their spectral is different from those of the large known background. This paper presents a method to detect man-made objects of hyperspectral images based on Projection Pursuit. Also evolutional algorithm was developed in order to find the optimal projection index.