Improved Fuzzy Clustering using Ensemble based Differential Evolution for Remote Sensing Image

Urban settlements in developing countries are, at present, growing five times as fast as those in developed countries. This paper presents the urban expansion and land use/land cover changes in the fast urbanizing coastal area of the Dakshina Kannada district in Karnataka state, South India, during the years 1983-2008 as a case study. Six Indian Remote Sensing (IRS) satellite images were used in the present work. Supervised classification was carried out using maximum likelihood algorithm. The overall accuracy of the classification varied from 79% to 86.6%, and the kappa statistics varied from 0.761 to 0.850. The results indicate that the urban/built-up area in the study area has almost tripled during the study period. During the same time, the population has increased by 215%. The major driving forces for the urbanization were the enhanced economic activity due to the port and industrialization in the area. The urban/built-up area is projected to increase to 381 km 2 and the population in the study area is expected to reach 2.68 million by the year 2028. Urban growth prediction helps urban planners and policymakers provide better infrastructure services to a huge number of new urban residents.

Towards Improving Self-adaptive Differential Evolution for Numerical Optimization: A Progress Note

The launch of the first Indian Remote Sensing Satellite in 1988 ushered in a new era of utilization of satellite data and technology in natural resources monitoring, coastal area management, disaster mitigation, weather and climate change studies. India with its very long coast line, areal extent, resources and population would have been a huge challenge to the coastal researchers and administrators if not for the Indian Remote Sensing satellite data that provided the much needed temporal, thematic information for effective mapping, monitoring and management of the coastal areas. The country’s large and diverse coastal regions are economically important and ecologically rich but also extremely vulnerable and highly prone to natural and anthropogenic hazards. Through the last three decades, the Indian remote sensing programme has contributed immensely to our understanding of the coast and ocean processes, coastal ecosystems, habitats, marine pollution and the vulnerability of the coast to natural hazards. IRS-1C with its improved spatial resolutions, additional spectral band, improved repetitively ushered in a new era of research specifically in the management of coastal areas and resources. Availability of spatial data at various resolutions, low cost, advances in technology resulted in new applications for management coastal areas. IRS-1C also can be aptly termed as the precursor for thematic satellite series such as OCEANSAT, RESOURCESAT and CARTOSAT, thereby setting the environs for thematic research in coastal areas. Integrated utilization of remote sensing data, numerical models, GIS and GPS has paved the way for the development of operation systems for management of coastal areas, thereby realizing the vision of Dr. Vikram Sarabhai, the father of Indian Space Programme, of building a self-reliant and indigenous Space Programme for the betterment of quality of life of the common people.

The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation for the candidates is used for DE. A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi- Objective Clustering with an unknown number of clusters K) is also undertaken. Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.

Chapter 16 - Remote Sensing in Lineament Identification: Examples from Western India

IR Earth horizon sensors are used for attitude determination of satellites. Simulation of IR horizon and ground calibration are major problems for testing sensors used in low altitude orbits due to geometrical constraints. A large earth simulator and a test facility with a 3500 mm. diameter disc have been developed for the calibration and the qualification of different types of IR earth sensors used on-board Indian Remote Sensing (IRS) satellites. The unique design of the test facility enables the simulation of the satellite altitudes, roll and pitch angles, IR radiation from space and Earth, gradients due to seasonal changes in radiance and thermo-vacuum environments of the sensor under test. The sensor performance can be tested either in open air conditions or under clean vacuum conditions at different temperatures of the sensor. The gradient of the Earth's IR radiation which changes with latitudes and seasons, and the Sun and the Moon interference can also be simulated. A computer based data acquisition system along with the instrumentation is used for controlling and monitoring vacuum, linear and rotary motions, temperature distribution on the earth disc and also for monitoring and recording of the sensor outputs. This paper describes the methodology behind various simulations and the testing involved. The constructional details of the large earth simulator and the alignment of the sensor with respect to the earth simulator are explained. A few of the actual sensor test results obtained are also included.

Differential Evolution with exponential crossover can be also competitive on numerical optimization

Differential Evolution (DE) is a very simple but powerful Evolutionary Algorithm (EA) for real-world applications. There are two aspects affecting the overall optimization performance significantly, one is parameter control scheme and the other is trial vector generation strategy. As all the control parameters are employed in generating trial vectors, from this perspective of view, the trial vector generation strategy dominates the overall optimization performance. Therefore, trial vector generation strategies are often designed first, and then the corresponding parameter control schemes are well-tuned thereafter. Both of them constitute the main body of a new DE variant. Here in the paper, a novel DE variant with a new designed trial vector generation strategy as well as novel parameter control scheme is proposed to tackle freight trains scheduling problem. By incorporating both a time-stamp mechanism of the external archive and a novel parameter control scheme, the new algorithm can secure better optimization performance not only on man-made benchmarks but also on real-world applications. Both the man-made benchmarks from Congress on Evolutionary Computation (CEC) test suite and the real-world freight train scheduling problem are employed in the validation of the new DE variant. The experiment results show that it is competitive with other state-of-the-art DE techniques for these optimization problems.

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index (MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.

Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

Geospatial insights into chronic bronchitis: Evaluating hotspots and environmental factors in MUDHRA-cohort of Mysuru district, India

Differential evolution with orthogonal array‐based initialization and a novel selection strategy

Enhancing the search ability of differential evolution through orthogonal crossover

The noise correction and image enhancements are done at the Data Processing Generation System (DPGS) at the National Remote Sensing Center (NRSC) satellite data production chain. Even after the noise correction algorithms, pixel noise is observed at the checkout by the Product Quality Control (PQC) system. These are detected manually at PQC and an alert is raised at the DPGS to recorrect the image. This process is time consuming due to the manual intervention and the application of noise correction algorithm on the entire image. This manuscript proposes a novel 2-stage, sliding window-based algorithm to automatically detect pixel noise in satellite images and localise the noise pixels so that the correction algorithms can be applied in a localised fashion. This local noise correction also restores the overall SNR of the image compared to global correction. This algorithm is realized and tested on data obtained from the Indian remote sensing (IRS) satellites like Cartosat-2S, Resourcesat-2/2A. The dataset is not open-sourced, and hence very minimal information is provided regarding the IRS data. However, we use Landsat-8 data to conduct a few analyses on the algorithm's performance. The role of patch size considered during the detection of pixel noise in satellite data is also analyzed.