An Efftcient Content Based Remote Sensing Image Retrieval Using Artiftcial Neural Network

2020 ◽  
Author(s):  
LP Aswathi ◽  
K Anoop
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Retrieval ◽  
Remote Sensing Image

Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

2018 ◽  
Vol 15 (10) ◽  
pp. 1535-1539 ◽  
Author(s):  
Famao Ye ◽  
Hui Xiao ◽  
Xuqing Zhao ◽  
Meng Dong ◽  
Wei Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Remote Sensing Image ◽  
Weighted Distance

scholarly journals Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

2021 ◽  
Vol 9 (VIII) ◽  
pp. 312-3189
Author(s):  
Chippy Babu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Remote Sensing Image ◽  
Similarity Criteria ◽  
Query Image ◽  
Data Set ◽  
Weighted Distance ◽  
Basic Features

Remote sensing image retrieval (RSIR) may be a fundamental task in remote sensing. Most content-based image retrieval (CBRSIR) approaches take an easy distance as similarity criteria. A retrieval method supported weighted distance and basic features of Convolutional Neural Network (CNN) is proposed during this letter. the strategy contains two stages. First, in offline stage, the pretrained CNN will be fine-tuned by some labelled images from our target data set, then accustomed extract CNN features, and labelled the pictures within the retrieval data set. Second, in online stage, we extract features of the query image by using fine-tuned CNN model and calculate the load of every image class and apply them to calculate the space between the query image and also the retrieved images. Experiments and methods are conducted on two Remote Sensing Image Retrieval data sets. Compared with the state-of the-art methods, the proposed method significantly improves retrieval performance.

Remote Sensing Image Retrieval Based on Convolutional Neural Network and Modified Fuzzy C-Means

2018 ◽  
Vol 55 (9) ◽  
pp. 091008
Author(s):  
彭晏飞 Peng Yanfei ◽  
宋晓男 Song Xiaonan ◽  
訾玲玲 Zi Lingling ◽  
王伟 Wang Wei
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Remote Sensing Image ◽  
Fuzzy C Means

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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