A novel active learning method for content based remote sensing image retrieval

2015 ◽  
Author(s):  
Begum Demir ◽  
Lorenzo Bruzzone
Keyword(s):  
Remote Sensing ◽  
Active Learning ◽  
Image Retrieval ◽  
Remote Sensing Image ◽  
Learning Method ◽  
Active Learning Method

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.

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

2009 ◽  
Vol 30 (4) ◽  
pp. 1045-1065 ◽  
Author(s):  
H. Taheri Shahraiyni ◽  
S. Bagheri Shouraki ◽  
F. Fell ◽  
M. Schaale ◽  
J. Fischer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Active Learning ◽  
Learning Method ◽  
Remote Sensing Reflectance ◽  
Active Learning Method ◽  
Reflectance Data
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