AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images

2019 ◽  
pp. 375-386 ◽  
Author(s):  
Qiankun Ye ◽  
Xiankai Lu ◽  
Hong Huo ◽  
Lihong Wan ◽  
Yiyou Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Optical Remote Sensing ◽  
Remote Sensing Images

scholarly journals Region search based on hybrid convolutional neural network in optical remote sensing images

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771985203 ◽  
Author(s):  
Shoulin Yin ◽  
Ye Zhang ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Remote Sensing Data ◽  
Search Method ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Candidate Regions ◽  
Region Search ◽  
Fast Region

Currently, big data is a new and hot issue. Particularly, the rapid growth of the Internet of Things causes a sharp growth of data. Enormous amounts of networking sensors are continuously collecting and transmitting data to be stored and processed in the cloud, including remote sensing data, environmental data, and geographical data. And region is regarded as the very important object in remote sensing data, which is mainly researched in this article. Region search is a crucial task in remote sensing process, especially for military area and civilian fields. It is difficult to fast search region accurately and achieve generalizability of the regions’ features due to the complex background information, as well as the smaller size. Especially, when processing region search in large-scale remote sensing image, detailed information as the feature can be extracted in inner region. To overcome the above difficulty region search task, we propose an accurate and fast region search in optical remote sensing images under cloud computing environment, which is based on hybrid convolutional neural network. The proposed region search method partitioned into four processes. First, fully convolutional network is adopted to produce all the candidate regions that contain the possible object regions. This process avoids exhaustive search for input images. Then, the features of all candidate regions are extracted by a fast region-based convolutional neural network structure. Third, we design a new difficult sample mining method for the training process. At the end, in order to improve the region search precision, we use an iterative bounding box regression algorithm to normalize the detected bounding boxes, in which the regions contain candidate objects. The proposed algorithm is evaluated on optical remote sensing images acquired from Google Earth. Finally, we conduct the experiments, and the obtained results show that the proposed region search method constantly achieves better results regardless of the type of images tested. Compared with traditional region search methods, such as region-based convolutional neural network and newest feature extraction frameworks, our proposed methods show better robustness with complex context semantic information and backgrounds.

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