scholarly journals Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

2019 ◽  
Vol 5 (3) ◽  
pp. 33 ◽  
Author(s):  
Pouria Sadeghi-Tehran ◽  
Plamen Angelov ◽  
Nicolas Virlet ◽  
Malcolm Hawkesford
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Feature Fusion ◽  
Search Time ◽  
Data Retrieval ◽  
Plant Biology ◽  
Data Generation ◽  
Imaging Data ◽  
Nested Structure

Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.

scholarly journals Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

2021 ◽  
Vol 13 (23) ◽  
pp. 4786
Author(s):  
Zhen Wang ◽  
Nannan Wu ◽  
Xiaohan Yang ◽  
Bingqi Yan ◽  
Pingping Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binary Code ◽  
Satellite Observation ◽  
Cross Entropy ◽  
Retrieval Task ◽  
Search Tasks ◽  
Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

scholarly journals Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3501
Author(s):  
Qingsong Xu ◽  
Xin Yuan ◽  
Chaojun Ouyang ◽  
Yue Zeng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Large Scale ◽  
Feature Fusion ◽  
Spectral Information ◽  
Spatial Problem ◽  
Remote Sensing Images ◽  
Heavy Weight

Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; (ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and (iii) cross-scale attention in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial-spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial-spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.

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 A New Approach for Large-Scale Scene Image Retrieval Based on Improved Parallelk-Means Algorithm in MapReduce Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Jianfang Cao ◽  
Min Wang ◽  
Hao Shi ◽  
Guohua Hu ◽  
Yun Tian
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Image Data ◽  
Data Retrieval ◽  
Single Node ◽  
New Approach ◽  
Scene Image ◽  
Computational Performance ◽  
Traditional Image

The rapid growth of digital images has caused the traditional image retrieval technology to be faced with new challenge. In this paper we introduce a new approach for large-scale scene image retrieval to solve the problems of massive image processing using traditional image retrieval methods. First, we improved traditionalk-Means clustering algorithm, which optimized the selection of the initial cluster centers and iteration procedure. Second, we presented a parallel design and realization method for improvedk-Means algorithm applied it to feature clustering of scene images. Finally, a storage and retrieval scheme for large-scale scene images was put forward using the large storage capacity and powerful parallel computing ability of the Hadoop distributed platform. The experimental results demonstrated that the proposed method achieved good performance. Compared with the traditional algorithms with single node architecture and parallelk-Means algorithm, the proposed method has obvious advantages for use in large-scale scene image data retrieval in terms of retrieval accuracy, retrieval time overhead, and computational performance (speedup and efficiency, sizeup, and scaleup), which is a significant improvement from applying parallel processing to intelligent algorithms with large-scale datasets.

scholarly journals A Lightweight Object Detection Framework for Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 683
Author(s):  
Lang Huyan ◽  
Yunpeng Bai ◽  
Ying Li ◽  
Dongmei Jiang ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Feature Fusion ◽  
Computational Cost ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Low Level

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

Sign in / Sign up

Export Citation Format

Share Document