Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model

2012 ◽  
Vol 9 (1) ◽  
pp. 109-113 ◽  
Author(s):  
Hao Sun ◽  
Xian Sun ◽  
Hongqi Wang ◽  
Yu Li ◽  
Xiangjuan Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Sparse Coding ◽  
Bag Of Words ◽  
Remote Sensing Images ◽  
Automatic Target Detection

scholarly journals Bag of words KAZE (BoWK) with two‐step classification for high‐resolution remote sensing images

2019 ◽  
Vol 13 (4) ◽  
pp. 395-403 ◽  
Author(s):  
Usman Muhammad ◽  
Weiqiang Wang ◽  
Abdenour Hadid ◽  
Shahbaz Pervez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Bag Of Words ◽  
Remote Sensing Images

scholarly journals Target detection from high-resolution remote sensing images using deep learning methods

2019 ◽  
Vol 11 (1) ◽  
pp. 48-64
Author(s):  
نیما فرهادی ◽  
عباس کیانی ◽  
حمید عبادی
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Target Detection ◽  
Remote Sensing Images ◽  
Learning Methods

scholarly journals SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 195-203
Author(s):  
Weihong Cui ◽  
Guofeng Wang ◽  
Chenyi Feng ◽  
Yiwei Zheng ◽  
Jonathan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Image Interpretation ◽  
Cluster Method ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Target Extraction

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 k-means 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.

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