The technology of airborne Light Detection And Ranging（LIDAR） receives wider attention and broader application for the ability of rapid acquiring three-dimensional topographic measurements of large-scale areas.These measurements are three-dimensional point clouds with irregular spacing.The points include bare ground

buildings

vehicles

vegetation and so on.It is important to identify and classify ground and non-ground points for generating DEM and extracting objects.Removing non-ground points from LIDAR datasets is called as filtering.In the last few years

a number of filtering algorithms have been explored.But most algorithms have more or less drawbacks and limitation in adaptability and correctness.Filtering is still a challenging task that is difficult to resolve for scene complexity.The topographic theory that filtering lies on are generally two aspects: one is that the natural terrain has continuity;the other is that the size of objects often has a range.Filtering based on mathematical morphology is considered as a promising strategy because it combines the two above aspects.But most of researchers carry on erosion or opening operation using every point

which is time-consuming and often cause errors.In order to overcome the weakness mentioned above

a new method of filtering based on morphological gradient is proposed in this paper.The method mainly analyzes the distribution characteristic of LIDAR points according to morphological gradients

so as to choose the specific points to carry on the morphological operation

which mainly include following steps.Firstly

point clouds are divided by an index mesh

which can organize points effectively and maintain the high resolution potential of raw data.Then

the morphological gradient of each point is calculated using the method suitable for filtering

and the low outliers are removed.Finally

some points are chosen based on gradients to carry on an improved opening operation iteratively.The iterative times are controlled through analyzing the gradient histogram.During each time of iteration

a point is classified as an object point if its difference of the height after opening operation and the original height is more than a threshold.15 sample data sets are released by ISPRS especially for testing of filtering algorithms

mainly including situations when difficulties are encountered in different geographical environments

such as outliers

object complexity

attached objects

vegetation and discontinuities in the bare ground.The semi-automatic filtering and manual editing of sample data have been done by ISPRS

whose results are used to evaluate result of automatic algorithms.ISPRS also publish the test results and analysis of eight typical filtering algorithms.The method proposed in this paper is tested with the sample data and compared with other filtering methods qualitatively and quantitatively.Qualitative assessment is done by visual representation of filtering results.Quantitative assessment is done by evaluating Type Ⅰ error（rejection of bare ground points）

Type Ⅱ error（acceptance of object points as bare ground） and Total error.The experimental results show that the method has high robustness in all kinds of complex scenes.The filter based on morphological gradient can reduce the nonessential computation as well as the possibility that errors happen.All types of error are controlled simultaneously in a relatively small range.The topographic features are well preserved while object points are removed effectively.So the method has good reliability and practicability.