A remote-sensing image-retrieval model based on an ensemble neural networks

With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites,the volume of image datasets is growing exponentially.The management of big Earth data is also becoming increasingly complex and difficult,with the result that it can be hard for users to access the imagery that they are interested in quickly,efficiently and intelligently.To address these challenges,this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks.This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remotesensing images and keep model simple.The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision.In addition,the coverage rate and mean precision show a dramatic improvement of more than 40%compared with existing methods based on normal way.And,the coverage ratio gets 86%for the top 10 return results.

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.