Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

et al. 2019

Self Cite

365

255

This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at http://bigearth.net/#downloads, offering an important resource to guide future progress on RS image analysis. Index Terms-Sentinel-2 multispectral images, Land cover land use, Multi-label image classification, Deep neural network, Remote sensing ! arXiv:2001.06372v2 [cs.CV]

Section: Introductionmentioning

confidence: 99%

Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

Sümbül

Charfuelàn

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

et al. 2019

Self Cite

365

255

“…Advances in satellite technology have resulted in significant growth in the volume of remote sensing (RS) data [1]. Accordingly, classification of RS image scenes, which are usually achieved by direct supervised classification of each image in the archive, has received extensive attention in RS [2,3].…”

Section: Introductionmentioning

confidence: 99%

Approximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classification

Byju

Sümbül

Image and Signal Processing for Remote Sensing XXV

et al. 2019

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This paper presents a novel approach based on the direct use of deep neural networks to approximate wavelet subbands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that utilizes the coarser level binary decoded wavelet sub-bands to approximate the finer level wavelet sub-bands (the image itself) through a series of deconvolutional layers. The second step aims to describe the high-level semantic content of the approximated wavelet subbands and to perform scene classification based on the learnt descriptors. This is achieved by: i) a series of convolutional layers for the extraction of descriptors which models the approximated sub-bands; and ii) fully connected layers for the RS image scene classification. Then, we introduce a loss function that allows to learn the parameters of both steps in an end-to-end trainable and unified neural network. The proposed approach requires only the coarser level wavelet sub-bands as input and thus minimizes the amount of decompression applied to the compressed RS images. Experimental results show the effectiveness of the proposed approach in terms of classification accuracy and reduced computational time when compared to the conventional use of Convolutional Neural Networks within the JPEG 2000 compressed domain.

“…Due to the continuous advances in satellite technology, recent years have witnessed an explosive growth of remote sensing (RS) image archives. Accordingly, fast and accurate content based image search and retrieval (CBIR) has attracted increasing attention in RS, aiming to seek the most similar images to a query image from large-scale archives [1]. Conventional RS image retrieval techniques often relies on the keywords/metadata associated with the images.…”

Section: Introductionmentioning

confidence: 99%

A novel coarse-to-fine remote sensing image retrieval system in JPEG-2000 compressed domain

Byju

Image and Signal Processing for Remote Sensing XXIV

Bruzzone

2018

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This paper presents a novel content-based image search and retrieval (CBIR) system that achieves coarse to fine remote sensing (RS) image description and retrieval in JPEG 2000 compressed domain. The proposed system initially: i) decodes the code-streams associated to the coarse (i.e., the lowest) wavelet resolution, and ii) discards the most irrelevant images to the query image that are selected based on the similarities estimated among the coarse resolution features of the query image and those of the archive images. Then, the codestreams associated to the sub-sequent resolution of the remaining images in the archive are decoded and the most irrelevant images are selected by considering the features associated to both resolutions. This is achieved by estimating the similarities between the query image and remaining images by giving higher weights to the features associated to the finer resolution while assigning lower weights to those related to the coarse resolution. To this end, the pyramid match kernel similarity measure is exploited. These processes are iterated until the code-streams associated to the highest wavelet resolution are decoded only for a very small set of images. By this way, the proposed system exploits a multiresolution and hierarchical feature space and accomplish an adaptive RS CBIR with significantly reduced retrieval time. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed system in terms of retrieval accuracy and time when compared to the standard CBIR systems.Keywords: image retrieval in compressed domain, JPEG 2000 algorithm, coarse to fine image retrieval, remote sensing.