A novel class sensitive hashing technique for large-scale content-based remote sensing image retrieval

2017 ◽  
Author(s):  
Begüm Demir ◽  
Lorenzo Bruzzone ◽  
Thomas Reato
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image

scholarly journals Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

2019 ◽  
Vol 11 (17) ◽  
pp. 2055 ◽  
Author(s):  
Xu Tang ◽  
Chao Liu ◽  
Jingjing Ma ◽  
Xiangrong Zhang ◽  
Fang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Content Organization ◽  
Hash Code ◽  
Improved Performance ◽  
Hashing Function ◽  
Image Archives ◽  
Class Variable

Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.

scholarly journals Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

2019 ◽  
Vol 12 (1) ◽  
pp. 101 ◽  
Author(s):  
Lirong Han ◽  
Peng Li ◽  
Xiao Bai ◽  
Christos Grecos ◽  
Xiaoyu Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Optimization Method ◽  
Remote Sensing Image ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Deep Hashing ◽  
Deep Model

Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval.

scholarly journals Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

2020 ◽  
Vol 12 (7) ◽  
pp. 1164 ◽  
Author(s):  
Jie Kong ◽  
Quansen Sun ◽  
Mithun Mukherjee ◽  
Jaime Lloret
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Complex Structure ◽  
Remote Sensing Image ◽  
Projection Matrix ◽  
Low Rank ◽  
Natural Image ◽  
Similarity Structure ◽  
Low Dimensional

As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks.

scholarly journals Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

2020 ◽  
Vol 12 (1) ◽  
pp. 175 ◽  
Author(s):  
Lili Fan ◽  
Hongwei Zhao ◽  
Haoyu Zhao
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Positive Sample ◽  
Fine Tuning ◽  
Remote Sensing Images ◽  
Hard Samples ◽  
Image Datasets

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

scholarly journals Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

2017 ◽  
Vol 6 (11) ◽  
pp. 364 ◽  
Author(s):  
Dongjie Ye ◽  
Yansheng Li ◽  
Chao Tao ◽  
Xunwei Xie ◽  
Xiang Wang
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Multiple Feature ◽  
Feature Hashing

Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks

2018 ◽  
Vol 56 (2) ◽  
pp. 950-965 ◽  
Author(s):  
Yansheng Li ◽  
Yongjun Zhang ◽  
Xin Huang ◽  
Hu Zhu ◽  
Jiayi Ma
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Deep Hashing
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