SPMK and Grabcut Based Target Extraction From High Resolution Remote Sensing Images

Abstract

ABSTRACT:Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the kmeans cluster method, we get the bag of visual words, and then, we adopt three levels' spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

“…e disadvantage is that when the light intensity changes and the lens moves rapidly, the histogram obtained will be distorted, which will lead to error detection. e pixel difference method has a low computational complexity, but it is very sensitive to pixel brightness changes and light changes caused by the motion of the camera equipment and objects in the film and television, which are likely to cause lens error detection [13,14]. In recent years, texture features [15] and scale-invariant feature conversion features [16] are also often found in literatures related to lens edge detection.…”