We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

/pdf/image-retrieval-ideas-influences-and-trends-of-the-new-age-51whw4w7su.pdf

Image retrieval: Ideas, influences, and trends of the new age

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

pdf/remote-sensing-image-scene-classification-benchmark-and-i89ggrxmk3.pdf

Remote Sensing Image Scene Classification: Benchmark and State of the Art

The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the "a trous" algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSAT/sup TM/ images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.

/pdf/multiresolution-based-image-fusion-with-additive-wavelet-2mxxbvuff6.pdf

Multiresolution-based image fusion with additive wavelet decomposition

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various data sets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning data sets and methods for scene classification is still lacking. In addition, almost all existing data sets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale data set, termed “NWPU-RESISC45,” which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This data set contains 31 500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 1) is large-scale on the scene classes and the total image number; 2) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion; and 3) has high within-class diversity and between-class similarity. The creation of this data set will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed data set, and the results are reported as a useful baseline for future research.

The authors present a concept of interactive learning and probabilistic retrieval of user-specific cover types in a content-based remote sensing image archive. A cover type is incrementally defined via user-provided positive and negative examples. From these examples, the authors infer probabilities of the Bayesian network that link the user interests to a pre-extracted content index. Due to the stochastic nature of the cover type definitions, the database system not only retrieves images according to the estimated coverage but also according to the accuracy of that estimation given the current state of learning. For the latter, they introduce the concept of separability. They expand on the steps of Bayesian inference to compute the application-free content index using a family of data models, and on the description of the stochastic link using hyperparameters. In particular, they focus on the interactive nature of their approach, which provides instantaneous feedback to the user in the form of an immediate update of the posterior map, and a very fast, approximate search in the archive. A java-based demonstrator using the presented concept of content-based access to a test archive of Landsat TM, X-SAR, and aerial images are available over the Internet [http:/www.vision.ee.ethz.ch/-rsia/ClickBayes].

Interactive learning and probabilistic retrieval in remote sensing image archives

We present a concept of interactive learning and probabilistic retrieval of user-specific cover types in a content-based remote sensing image archive. A cover type is incrementally defined via user-provided positive and negative examples. From these examples, we infer probabilities of the Bayesian network that link the user interests to a pre-extracted content index. Due to the stochastic nature of the cover type definitions, the database system not only retrieves images according to the estimated coverage but also according to the accuracy of that estimation given the current state of learning. For the latter, we introduce the concept of separability. We expand on the steps of Bayesian inference to compute the application-free content index using a family of data models, and on the description of the stochastic link using hyperparameters. In particular, we focus on the interactive nature of our approach, which provides instantaneous feedback to the user in the form of an immediate update of the posterior map, and a very fast, approximate search in the archive. A java-based demonstrator using the presented concept of content-based access to a test archive of Landsat TM, X-SAR, and aerial images are available over the Internet [http://www.vision.ee.ethz.ch/∼rsia/ClickBayes].

The progress in information retrieval, computer vision, and image analysis makes it possible to establish very complete bases of algorithms and operators. A specialist in remote sensing or image processing now has the tools that allow him, at least in theory, to configure applications solving complex problems of image understanding. However, in reality, earth observation (EO) data analysis is still performed in a very laborious way at the end of repeated cycles of trial and error. To overcome this, we proposed a novel advanced remote sensing information processing system knowledge-driven information mining (KIM). KIM is based on human-centered concepts (HCCs), which implements new features and functions allowing improved feature extraction, search on a semantic level, the availability of collected knowledge, interactive knowledge discovery, and new visual user interfaces. We assess the HCC methodology for solving several difficult tasks in EO image interpretation, using a broad variety of sensor data, from meter-resolution synthetic aperture radar and optical images to hyperspectral data.

Human-centered concepts for exploration and understanding of Earth observation images

Snow-cover mapping is a prerequisite for deriving the main input variable for a deterministic snowmelt runoff model (SRM). Operational forecasting of snowmelt runoff has to rely on "guaranteed snow cover information" for a specific area and time. Remote sensing can provide the necessary data, especially when different sensor systems are available for combined interpretations. It has been shown already how remote-sensing information from Landsat-MSS and NOAA/AVHRR supplement each other. It is important, though, that when using coarse spatial sensor resolution, special attention to the area, its topography, and the amount of snow coverage must be taken.

Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

The combined influence of increased temperatures and changed precipitation on the seasonal snow cover and runoff is evaluated for the Upper Rhine Basin at Felsberg (3250 km 2 , 560-3614 m a.s.l.). The runoff regime reflects the snow accumulation in the winter half-year and snowmelt in the summer half-year. The method of predicting future runoff regimes is based on high resolution periodical snow cover mapping by satellites. The snow coverage is the main input variable for the snowmelt runoff model SRM-ETH (version of the Institute of Technology ETH). The model computes the present runoff and the climate-affected snow-covered areas, from which it computes the future runoff. Conditions of the present climate are represented by a normalized year. Snow conditions and yearly hydrographs are evaluated for the years 2030 and 2100 according to the given climate scenarios for this region. Quantitative results indicate the decrease of the snow accumulation on 1 April as well as the seasonal redistribution and changes of runoff.

Effects of climate change on water resources and runoff in an Alpine basin

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Interactive learning and probabilistic retrieval in remote sensing image archives

Interactive learning and probabilistic retrieval in remote sensing image archives

Human-centered concepts for exploration and understanding of Earth observation images

Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

Effects of climate change on water resources and runoff in an Alpine basin