CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing

We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Deep Hashing Based on Class-Discriminated Neighborhood Embedding

Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community

Impact of Feature Representation on Remote Sensing Image Retrieval

Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task.  Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process

Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart