Incorporating Human Views into Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval

2020 ◽  
pp. 394-403
Author(s):  
Yishu Liu ◽  
Conghui Chen ◽  
Zhengzhuo Han ◽  
Yingbin Liu ◽  
Liwang Ding
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Transfer Learning ◽  
Remote Sensing Image

Similarity-Based Unsupervised Deep Transfer Learning for Remote Sensing Image Retrieval

2020 ◽  
Vol 58 (11) ◽  
pp. 7872-7889 ◽  
Author(s):  
Yishu Liu ◽  
Liwang Ding ◽  
Conghui Chen ◽  
Yingbin Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Transfer Learning ◽  
Remote Sensing Image

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

scholarly journals Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

2018 ◽  
Vol 10 (8) ◽  
pp. 1243 ◽  
Author(s):  
Xu Tang ◽  
Xiangrong Zhang ◽  
Fang Liu ◽  
Licheng Jiao
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Feature Learning ◽  
Remote Sensing Image ◽  
Code Word ◽  
Image Features ◽  
Learning Method ◽  
L1 Norm ◽  
Image Descriptor ◽  
Image Archives

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

An improved SVM model for relevance feedback in remote sensing image retrieval

2013 ◽  
Vol 7 (9) ◽  
pp. 725-745 ◽  
Author(s):  
Caihong Ma ◽  
Qin Dai ◽  
Jianbo Liu ◽  
Shibin Liu ◽  
Jin Yang
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Remote Sensing Image ◽  
Svm Model

Attention-Driven Cross-Modal Remote Sensing Image Retrieval

2021 ◽  
Author(s):  
Ushasi Chaudhuri ◽  
Biplab Banerjee ◽  
Avik Bhattacharya ◽  
Mihai Datcu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Remote Sensing Image
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