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

Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

Image processing is a rapidly growing research area of computer science and remains as a challenging problem within the computer vision fields. For the classification of flower images, the problem is mainly due to the huge similarities in terms of colour and texture. The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. The flower image is extracted through Region of Interest (ROI) in which texture and colour are emphasized in this study. In this research, a total of 960 images were extracted from 16 types of flowers. Each ROI was represented by three colour attributes (Hue, Saturation, and Value) and four textures attribute (Contrast, Correlation, Energy and Homogeneity). In training and testing phases, experiments were carried out to observe the classification performance of Neural Networks with duplication of difficult pattern to learn (referred to as DOUBLE) as this could possibly explain as to why some flower images were difficult to learn by classifiers. Results show that the overall performance of Neural Network with DOUBLE is 96.3% while actual data set is 68.3%, and the accuracy obtained from Logistic Regression with actual data set is 60.5%. The Decision Tree classification results indicate that the highest performance obtained by Chi-Squared Automatic Interaction Detection(CHAID) and Exhaustive CHAID (EX-CHAID) is merely 42% with DOUBLE. The findings from this study indicate that Neural Network with DOUBLE data set produces highest performance compared to Logistic Regression and Decision Tree. Therefore, NN has been potential in building Malaysian blooming flower model. Future studies can be focused on increasing the sample size and ROI thus may lead to a higher percentage of accuracy. Nevertheless, the developed flower model can be used as part of the Malaysian Blooming Flower recognition system in the future where the colours and texture are needed in the flower identification process

Coastal fog detection using visual sensing

Use of visual sensing techniques to detect low visibility conditions may have a number of advantages when combined with other methods, such as satellite based remote sensing, as data can be collected and processed in real or near real time. Camera-enabled visual sensing can provide direct confirmation of modelling and forecasting results. Fog detection, modelling and prediction are a priority for maritime communities and coastal cities due to economic impacts of fog on aviation, marine, and land transportation. Canadian and Irish coasts are particularly vulnerable to dense fog under certain environmental conditions. Offshore oil and gas production on Grand Bank (off the Canadian East Coast) can be adversely affected by weather and sea state conditions. In particular, fog can disrupt the transfer of equipment and people to/from the production platforms by helicopter. Such disruptions create delays and the delays cost money. According to offshore oil and gas industry representatives at a recent workshop on metocean monitoring and forecasting for the NL offshore, there is a real need for improved forecasting of visibility (fog) out to 3 days. The ability to accurately forecast future fog conditions would improve the industry’s ability to adjust its schedule of operations accordingly. In addition, it was recognized by workshop participants that the physics of Grand Banks fog formation is not well understood, and that more and better data are needed

Geological and structural analysis of the Hwange area-Northwest Zimbabwe: using remotely sensed data and geographic information systems (GIS)

There is a continuous need to locate more targets for coal exploration and evaluation of geological structures in the north-west coalfields in Zimbabwe. Conventional methods of analysing geological structures and field mapping are being hindered by inaccessibility of some areas and thick covers of Recent sediments. Remote sensing has been found to be a valuable method of identifying lithologic units and geological structures in the· area. Integration of the remotely sensed data in a 2D GIS resulted in recognition of spatial relationships between lithologic units, geological structures , coal seams and vegetation patterns. The Hwange area constitutes the western part of the Mid-Zambezi Karoo basin. The area consist of a wide spectrum of rocks ranging from Precambrian gneisses, Proterozoic schists and granulites, Karoo sediments to Tertiary and Recent sands. The area has been affected by a number of faults and shears some of which post date the Karoo sediments. These faults are an expression of the major tectonic events associated with this area. Some of the faults have been attributed to the effects of the Zambezi Rift System. Fault zones in the area, such as the Deka, Entuba and Inyantue Zones have been recognised as part of this system and these divide the Lower Karoo rocks into different coalfields. To try and evaluate the outcrop patterns and geological structures in the Hwange area, all the available geological and structural data were captured in a spatial database. The diversity of data incorporated in the spatial database demanded the need for a structured database design approach. The Entity-Relationship model was used to conceptualise the geological data of the ' Hwange area This model was transformed into the Relational Model that formed the implementation model of the database. Landsat 5 TM data covering the area from the Zimbabwean winter (20 June 1984) path 172, row 73 were also analysed for the information required to locate Karoo rift faults and the distribution of lithologic units associated with coal. The use of directional filters in the E-W and NE-SW directions and vegetation reflection characteristics during the dry season (June 1984) proved very effective in mapping fractures in the Karoo rocks. Landsat TM image enhancement techniques such as principal components analysis, edge enhancement, decorrelation stretching, band ratios; and colour composites made following these techniques, allowed mapping of different lithological units and discrimination between Karoo rocks and the crystalline basement rocks. Lineament analysis defined E-W, ENE-WSW, NE-SW and NW-SE conjugate sets of lineaments. The first three sets are related to the regional fracture zones of the Zambezi rift system The Entuba fault zone was found to be associated with most of the fractures affecting the Hwange coalfields. These have a dominant NE-SW and ENE-WSW trend in the Western Areas, Wankie Concession, Chaba, Entuba and Sinamatella coalfields. The E-W trending fracture set is dominated by joint sets in the Karoo basalt covering the north-west portion of the Hwange Coalfields. These show no relationship with the linear features of the Zambezi Rift system The NW-SE trending lineaments are dominantly developed on tilted bedding planes in the Karoo rocks as well as a few sparse joints in the Karoo basalt. Overlaying enhanced Landsat TM images on mapped faults and lithology data in a GIS revealed a number of features along the Entuba zone which were not previously known. The south-western part of the Entuba inlier was shown to consist of a synformal fold plunging to the south and bound on both sides by strike slip faults. Several kinematic indicators such as displacement of sedimentary strata have shown that the Entuba fault displays right lateral strike-slip coupled with dipslip movement. Proximity analysis using borehole data (depth to top and bottom of a coal seam) showed that most of the lineaments in the area are normal faults which have caused considerable displacements of the main coal seam Comparison of seam depth across most of these faults within coalfields and from one field to another shows that local and regional variations in depths of the main seam is primarily a function of vertical displacements along the faults over and above variations in the morphology of the pre-Karoo floor. The Entuba field was found to have greatest vertical variations over very short distances across faults, with depths varying from 60m to 520m from west to east over distances of less than 500m This part of the field has been partly affected by extensive normal faults, some of which can be traced for more than 10km. In the Hwange area, the Karoo rocks have been down faulted into a rift margin which is in turn divided into smaller fault blocks by intra-rift faulting. The shape of the fault blocks are further controlled by the orientation of the post-Karoo faults which have also down faulted the main coal seam Exploration activity in the area should also seek to establish the locations of these faults to help further decipher variations in depths of coal seams

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method\u27s experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously

Artificial Intelligence Based Classification for Urban Surface Water Modelling

Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration

Automatic Archeological Feature Extraction from Satellite VHR Images

Abstract Archaeological applications need a methodological approach on a variable scale able to satisfy the intra-site (excavation) and the inter-site (survey, environmental research). The increased availability of high resolution and micro-scale data has substantially favoured archaeological applications and the consequent use of GIS platforms for reconstruction of archaeological landscapes based on remotely sensed data. Feature extraction of multispectral remotely sensing image is an important task before any further processing. High resolution remote sensing data, especially panchromatic, is an important input for the analysis of various types of image characteristics; it plays an important role in the visual systems for recognition and interpretation of given data. The methods proposed rely on an object-oriented approach based on a theory for the analysis of spatial structures called mathematical morphology. The term ‘‘morphology’’ stems from the fact that it aims at analysing object shapes and forms. It is mathematical in the sense that the analysis is based on the set theory, integral geometry, and lattice algebra. Mathematical morphology has proven to be a powerful image analysis technique; two-dimensional grey tone images are seen as three-dimensional sets by associating each image pixel with an elevation proportional to its intensity level. An object of known shape and size, called the structuring element, is then used to investigate the morphology of the input set. This is achieved by positioning the origin of the structuring element to every possible position of the space and testing, for each position, whether the structuring element either is included or has a nonempty intersection with the studied set. The shape and size of the structuring element must be selected according to the morphology of the searched image structures. Other two feature extraction techniques were used, eCognition and ENVI module SW, in order to compare the results. These techniques were applied to different archaeological sites in Turkmenistan (Nisa) and in Iraq (Babylon); a further change detection analysis was applied to the Babylon site using two HR images as a pre–post second gulf war. We had different results or outputs, taking into consideration the fact that the operative scale of sensed data determines the final result of the elaboration and the output of the information quality, because each of them was sensitive to specific shapes in each input image, we had mapped linear and nonlinear objects, updating archaeological cartography, automatic change detection analysis for the Babylon site. The discussion of these techniques has the objective to provide the archaeological team with new instruments for the orientation and the planning of a remote sensing application. & 2009 Elsevier Ltd. All rights reserved