scholarly journals Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 291 ◽  
Author(s):  
Pingping Liu ◽  
Guixia Gou ◽  
Xue Shan ◽  
Dan Tao ◽  
Qiuzhan Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Deep Feature ◽  
Deep Embedding ◽  
Similarity Structure ◽  
Global Optimal ◽  
Triplet Loss ◽  
Mining Scheme

A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.

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 Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval

2020 ◽  
Vol 9 (2) ◽  
pp. 61
Author(s):  
Hongwei Zhao ◽  
Lin Yuan ◽  
Haoyu Zhao
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Image Retrieval ◽  
Learning Strategy ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Retention Loss ◽  
Deep Metric Learning ◽  
Hard Samples

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.

scholarly journals Enhancing remote sensing image retrieval using a triplet deep metric learning network

2019 ◽  
Vol 41 (2) ◽  
pp. 740-751 ◽  
Author(s):  
Rui Cao ◽  
Qian Zhang ◽  
Jiasong Zhu ◽  
Qing Li ◽  
Qingquan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Learning Network ◽  
Deep Metric Learning

scholarly journals A Novel Ensemble Architecture of Residual Attention-Based Deep Metric Learning for Remote Sensing Image Retrieval

2021 ◽  
Vol 13 (17) ◽  
pp. 3445
Author(s):  
Qimin Cheng ◽  
Deqiao Gan ◽  
Peng Fu ◽  
Haiyan Huang ◽  
Yuzhuo Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Global Features ◽  
Feature Vectors ◽  
Retrieval Efficiency ◽  
Deep Metric Learning ◽  
The Mean ◽  
Reduced Time

Recently, deep metric learning (DML) has received widespread attention in the field of remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to represent images and then to measure the similarity between images via learning a distance function among feature vectors. However, the distinguishability of features extracted by the most current DML-based methods for RSIR is still not sufficient, and the retrieval efficiency needs to be further improved. To this end, we propose a novel ensemble architecture of residual attention-based deep metric learning (EARA) for RSIR. In our proposed architecture, residual attention is introduced and ameliorated to increase feature discriminability, maintain global features, and concatenate feature vectors of different weights. Then, descriptor ensemble rather than embedding ensemble is chosen to further boost the performance of RSIR with reduced time cost and memory consumption. Furthermore, our proposed architecture can be flexibly extended with different types of deep neural networks, loss functions, and feature descriptors. To evaluate the performance and efficiency of our architecture, we conduct exhaustive experiments on three benchmark remote sensing datasets, including UCMD, SIRI-WHU, and AID. The experimental results demonstrate that the proposed architecture outperforms the four state-of-the-art methods, including BIER, A-BIER, DCES, and ABE, by 15.45%, 13.04%, 10.31%, and 6.62% in the mean Average Precision (mAP), respectively. As for the retrieval execution complexity, the retrieval time and floating point of operations (FLOPs), needed by the proposed architecture on AID, reduce by 92% and 80% compared to those needed by ABE, albeit with the same Recall@1 between the two methods.

scholarly journals Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval

2021 ◽  
Vol 13 (15) ◽  
pp. 2924
Author(s):  
Xue Shan ◽  
Pingping Liu ◽  
Yifan Wang ◽  
Qiuzhan Zhou ◽  
Zhen Wang
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Convergence Time ◽  
Aerial Image ◽  
Shooting Methods ◽  
Retrieval Method ◽  
Memory Space ◽  
Deep Hashing

With the improvement of various space-satellite shooting methods, the sources, scenes, and quantities of remote sensing data are also increasing. An effective and fast remote sensing image retrieval method is necessary, and many researchers have conducted a lot of work in this direction. Nevertheless, a fast retrieval method called hashing retrieval is proposed to improve retrieval speed, while maintaining retrieval accuracy and greatly reducing memory space consumption. At the same time, proxy-based metric learning losses can reduce convergence time. Naturally, we present a proxy-based hash retrieval method, called DHPL (Deep Hashing using Proxy Loss), which combines hash code learning with proxy-based metric learning in a convolutional neural network. Specifically, we designed a novel proxy metric learning network, and we used one hash loss function to reduce the quantified losses. For the University of California Merced (UCMD) dataset, DHPL resulted in a mean average precision (mAP) of up to 98.53% on 16 hash bits, 98.83% on 32 hash bits, 99.01% on 48 hash bits, and 99.21% on 64 hash bits. For the aerial image dataset (AID), DHPL achieved an mAP of up to 93.53% on 16 hash bits, 97.36% on 32 hash bits, 98.28% on 48 hash bits, and 98.54% on 64 bits. Our experimental results on UCMD and AID datasets illustrate that DHPL could generate great results compared with other state-of-the-art hash approaches.

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.

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