Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang’E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are estimated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis. Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.

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Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Lunar sourcebook. A user's guide to the moon

Texture features for image classification and retrieval.

A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia

Forest fires are an important threat to humans and other living creatures, with the development of satellite technology it can be constantly monitored and controlled. Presence of smoke in the atmosphere is the indication of forest wildfires. In fire alarm systems, fire detection plays a crucial part in avoiding damages and other fire disasters that lead to social ramifications. Avoiding large scale fire, effective fire detection from visual scenes is important. To improve fire detection accuracy, an effective approach of a convolutional neural network based Inception-v3 based on transfer learning is designed which train the satellite images and classify the datasets into a fire and non-fire images, confusion matrix is generated to specify efficiency of the framework, then extract the fire occurred region in the satellite image using local binary pattern it reduces false detection rates.

Deep Learning Based Forest Fire Classification and Detection in Satellite Images

Image fusion is a technique of obtaining high spatial resolution multispectral images from low spatial resolution multispectral and high spatial resolution panchromatic images. Various techniques exist to perform such fusion. These techniques, however, do not seem to preserve the spectral information content of original multispectral image in the fused image. Hence, in this study a recent and efficient technique of fusion based on wavelet transformation was attempted and its efficiency was compared with that of the conventional techniques. Accordingly, a lower resolution multispectral image (IRS-1C LISS -III) and a high resolution panchromatic (IRS-1C PAN) were fused using three conventional techniques: IHS (Intensity Hue and Saturation), PCA (Principal Component Analysis) and Brovey Transformation and the recently evolved wavelet transformation technique. The four outputs were evaluated using visual comparison, statistical correlation (using AOI and image to image correlation) and spectral separability studies. Among the conventional methods, PCA based fusion was found to be better than IHS and Brovey transformation techniques in improving visual interpretability and in preserving the original spectral information content. When compared with conventional techniques, the wavelet transformation technique proved to be a better option since it preserved most (85%) of the spectral information content and also improved visual appreciation. Spectral separability between five land cover classes was also highest for the wavelet transformation technique. It is believed that, since wavelet transformation operates in the frequency domain, it is able to preserve most of the original image content in a better fashion than the conventional techniques, which operate in the spatial domain. Hence, it is suggested that image fusion be attempted in the frequency domain.

Comparison of conventional and wavelet transform techniques for fusion of irs-1c liss-iii and pan images

Crater detection, classification and contextual information extraction in lunar images using a novel algorithm

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Surface defect inspection plays a vital role in leather manufacturing. Current practice involves an expert to inspect each piece of leather individually and detect defects manually. However, such a manual inspection is highly subjective and varies quite considerably from one assorter to another. Computer vision system for natural material like leather is a challenging research problem. This study describes the application of computer vision system to capture leather surface images and use of a novel Fast Convergence Particle Swarm Optimization (FCPSO) algorithm on a set of handcrafted texture features viz., GLCM and classified using supervised classifiers viz., Multi Layer Perceptron (MLP), Decision Tree (DT), SVM, Naive Bayes, K-Nearest Neighbors (KNN) and Random Forest (RF). FCPSO using modified fitness function by selective band Shannon entropy is implemented to segment industrial leather images. Segmentation efficiency of the proposed FCPSO algorithm is evaluated and its performance is compared with other optimization algorithms. Efficiency of the segmentation algorithms is evaluated using performance measures such as average difference (AD), Area Error Rate (AER), Edge-based structural similarity index (ESSIM), F-Score, Normalized correlation coefficient (NK), Overlap Error (OE), structural content (SC), Structural similarity index (SSIM) and Zijdenbos similarity index (ZSI). Correlation of the segmented area using FCPSO with the experts’ ground truth is found to be high with R value of 0.84. Feature extraction is carried out using GLCM texture features and the most prominent features were selected using statistical t-test and correlation coefficient. Experimental results showed encouraging results for random forest classifier confirming the potential of the proposed system for automatic leather defect classification.

K. Vani

Papers

Deep Learning Based Forest Fire Classification and Detection in Satellite Images

Comparison of conventional and wavelet transform techniques for fusion of irs-1c liss-iii and pan images

Crater detection, classification and contextual information extraction in lunar images using a novel algorithm

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach