Change detection for high-resolution remote sensing imagery based on multi-scale segmentation and fusion

2017 ◽  
Author(s):  
Qingle Guo ◽  
Junping Zhang ◽  
Tong Li ◽  
Xiaochen Lu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Imagery ◽  
Multi Scale

High-resolution optical remote sensing imagery change detection through deep transfer learning

2019 ◽  
Vol 13 (04) ◽  
pp. 1 ◽  
Author(s):  
Mohammed El Amin Larabi ◽  
Souleyman Chaib ◽  
Khadidja Bakhti ◽  
Kamel Hasni ◽  
Mohammed Amine Bouhlala
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Transfer Learning ◽  
Optical Remote Sensing ◽  
Remote Sensing Imagery

scholarly journals BUILDINGS CHANGE DETECTION BASED ON SHAPE MATCHING FOR MULTI-RESOLUTION REMOTE SENSING IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 683-687 ◽  
Author(s):  
M. Abdessetar ◽  
Y. Zhong
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Urban Area ◽  
Damage Assessment ◽  
Euclidean Distance ◽  
Shape Matching ◽  
Satellite Images ◽  
Remote Sensing Imagery ◽  
Multi Scale ◽  
Evolution Study

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object’s shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T<sub>0</sub> to shape T<sub>1</sub> and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).

scholarly journals Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

2019 ◽  
Vol 11 (7) ◽  
pp. 755 ◽  
Author(s):  
Xiaodong Zhang ◽  
Kun Zhu ◽  
Guanzhou Chen ◽  
Xiaoliang Tan ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Large Scale ◽  
Training Data ◽  
Validation Dataset ◽  
Remote Sensing Imagery ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Pyramid

Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset.

scholarly journals OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

2016 ◽  
Vol XLI-B7 ◽  
pp. 517-522
Author(s):  
Yonghong Jia ◽  
Mingting Zhou ◽  
Ye Jinshan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Multi Scale ◽  
Single Scale ◽  
High Resolution Images ◽  
Image Object ◽  
Scale Classification

The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

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