Geospatial Target Detection from High-Resolution Remote-Sensing Images Based on PIIFD Descriptor and Salient Regions

2019 ◽  
Vol 47 (5) ◽  
pp. 879-891
Author(s):  
Fariborz Ghorbani ◽  
Hamid Ebadi ◽  
Amin Sedaghat
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Remote Sensing Images ◽  
Salient Regions

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.

scholarly journals Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8113
Author(s):  
Kun Fang ◽  
Jianquan Ouyang ◽  
Buwei Hu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Target Prediction ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Average Precision ◽  
Detection Model ◽  
Feature Information ◽  
Small Targets

Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations.

scholarly journals Vehicle Detection in Very-High-Resolution Remote Sensing Images Based on an Anchor-Free Detection Model with a More Precise Foveal Area

2021 ◽  
Vol 10 (8) ◽  
pp. 549
Author(s):  
Xungen Li ◽  
Feifei Men ◽  
Shuaishuai Lv ◽  
Xiao Jiang ◽  
Mian Pan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Detection Method ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Background Information ◽  
Remote Sensing Images ◽  
Feature Pyramid ◽  
Very High

Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast.

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