Visual attention based model for target detection in high resolution remote sensing images

2012 ◽  
Author(s):  
Xin Ke ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Target Detection ◽  
Remote Sensing Images

Visual attention model based mining area recognition on massive high-resolution remote sensing images

2015 ◽  
Vol 18 (2) ◽  
pp. 541-548 ◽  
Author(s):  
Xiaolu Song ◽  
Guojin He ◽  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Yan Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Mining Area ◽  
Remote Sensing Images ◽  
Visual Attention Model ◽  
Attention Model ◽  
Model Based

scholarly journals Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Linyi Li ◽  
Tingbao Xu ◽  
Yun Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Fuzzy Classification ◽  
Accuracy Evaluation ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Quantitative Accuracy ◽  
Extraction Algorithm

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution 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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