Lavika Goel

Hybrid bio-inspired techniques for land cover feature extraction: A remote sensing perspective

Recent advances in the theoretical and practical implementations of biogeography have led to the exploration of new bio-inspired techniques which can prove to be the building blocks of hybrid bio-inspired techniques. This aspect was discovered while considering the exploration of bio-inspired intelligence for developing generic optimization algorithms that can be adapted for performing the given land cover feature extraction task at hand. Certain bio-inspired techniques when integrated with the existing optimization techniques can drastically improve their optimization capability hence leading to better feature extraction. In this paper, we propose a generic architectural framework of a hybrid biologically inspired technique that is characterized by its capability to adapt according to the database of expert knowledge for a more efficient, focused and refined feature extraction. Since our hybrid feature extractor possesses intelligence for selective cluster identification for application of either of the constituent techniques which is in turn based on an inefficiency analysis, we term our classifier as the hybrid bio-inspired pattern analysis based intelligent classifier. Our hybrid classifier combines the strengths of the modified BBO Technique for land cover feature extraction with the Hybrid ACO2/PSO Technique for a more refined land cover feature extraction. The algorithm has been tested for for the remote sensing application of land cover feature extraction where we have applied it to the 7-Band carto-set satellite image of size 472x546 of the Alwar area in Rajasthan and gives far better feature extraction results than the original biogeography based land cover feature extractor [20] and the other soft computing techniques such as ACO, Hybrid PSO-ACO2, Hybrid ACO-BBO Classifier, Fuzzy sets, Rough-Fuzzy Tie up etc.. The 7-band Alwar Image is a benchmark image for testing the performance of a bio-inspired classifier on multi-spectral satellite images since this image is a complete image in the sense that it contains all the land cover features that we need to extract and hence land cover feature extraction results are demonstrated and compared using this image as the standard image.

Taxonomy of nature inspired computational intelligence: A remote sensing perspective

The concepts in geospatial sciences are generally vague, ambiguous and imprecise. Also, a combination of spectral, spatial and radiometric resolution of space-borne sensors presents a selective and incomplete look of the geospatial feature/object under its view from the space. Recently, the nature inspired computational intelligence (CI) techniques have emerged as an efficient mechanism to handle diverse uncertainty characteristics. This paper proposes that the human-mind model based computational intelligence techniques, the artificial immune system based computational intelligence techniques; the swarm intelligence based computational intelligence techniques and the emerging geo-sciences based intelligent techniques can be considered as the four pillars of nature inspired CI techniques and hence redefines and extends the taxonomy of nature inspired CI. Researchers have shown keen interest on the applications of natural computing in divergent domains. Scanty references are available on the applications of nature inspired computing in the area of remote sensing. We hence also propose the taxonomy of the most recent nature inspired CI techniques that have been adapted till date for geo-spatial feature extraction and analyze their performances. We also construct a technology timeline of these recent nature inspired CI techniques.

Dynamic Model of Blended Biogeography Based Optimization for Land Cover Feature Extraction

This paper presents a dynamic model of the blended biogeography based optimization (BBO) for land cover feature extraction. In the blended BBO, the habitats represent the candidate problem solutions and the species migration represents the sharing of features (SIVs) between candidate solutions according to the fitness of the habitats which is called their HSI value [9]. However, it is assumed that these SIVs i.e. the number of solution features, remain constant for every habitat [10] and the HSI for each habitat depends only on the immigration and the emigration rates of species [9]. This paper extends the blended BBO by considering the fact that the no. of SIVs or the decision variables may not remain constant for all candidate solutions (habitats) that are part of the Universal habitat. Since the characteristics of each habitat vary greatly hence, comparing all the habitats using the same set of SIVs may be misleading and also may not lead to an optimum solution. Hence, in our dynamic model, we consider the fact that HSI of a solution is affected by factors other than migration of SIVs i.e. solution features, also. These other factors can be modeled as several definitions of HSI of a habitat, each definition based on a different set of SIVs which simulates the effect of these other factors. We demonstrate the performance of the proposed model by running it on the real world problem of land cover feature extraction in a multi-spectral satellite image.

Performance governing factors of biogeography based land cover feature extraction: An analytical study

In recent years, nature inspired remote sensing image classification has become a global research area for acquiring the geo-spatial information from satellite data. The findings of recent studies are showing strong evidence to the fact that various classifiers perform differently when applied to images having different natural terrain features. This paper is an analytical study and a performance based characterization of the most recent nature inspired image classification technique i.e. Biogeography based Optimization (BBO) that has been used for focused land cover feature extraction [6]. The paper explores the behavior of BBO over different terrain features of a multi-spectral satellite image and establishes the fact that the classification efficiency of BBO for a given land cover feature is proportional to the degree of disorder of the Digital number (DN) values of the pixels comprising that land cover feature when viewed in any of the bands of the multi-spectral satellite image. More precisely, the classification efficiency of BBO on a terrain feature is inversely proportional to the entropy for that feature when viewed in any of the bands of the multi-spectral satellite image. For verification, we calculated the entropies for each of the land cover feature in two bands and found the same results in both the bands, which proves our proposed concept. The dataset on which the proposed concept is demonstrated is the 7-band cartoset satellite image of size 472 × 576 pixels of the Alwar region in Rajasthan. The results indicate that BBO is able to classify the homogeneous regions i.e. the regions with the lower entropy, more efficiently than the regions which show a greater degree of heterogeneity, i.e. higher entropy.

Biogeography and geo-sciences based land cover feature extraction

This work proposes a biogeography and geo-sciences based soft computing technique which is an extension of original biogeography based feature extraction algorithm using the concept of entropy inspired from the geo-sciences phenomenon of mantle convection and dynamics of the earth. This algorithm uses surface entropy in the relevant band of multi-spectral images as the basis of calculating the habitat suitability index which in turn forms the basis of identifying different terrain features in the satellite image. The proposed work has been primarily developed for the purpose of finding the applications of geo-sciences in developing computationally intelligent models. This may lead to another concept of process randomization, generation of virtual scenarios, etc. which are important ingredients in battlefield assessment. The proposed feature extractor algorithm has been applied on the datasets of Alwar region in Rajasthan and Patalganga area in Shivalik ranges. The results indicate that our proposed geo-sciences based classifier is highly efficient in extracting land cover features. Further when integrated with hybrid bio-inspired intelligent classifier proposed in our previous work, it improves its classification efficiency and outperforms the earlier probabilistic classifiers, recent soft computing classifiers such as membrane computing, hybrid FPAB/BBO, extended non-linear BBO, etc. and the very recent hybrid ACO2/PSO/BBO classifier proposed by us [16,21]. Our results conclude that the classifier based on our proposed model is the best known classifier developed till date. The proposed model is flexible and can adapt itself to suit to a large number of classification problems including mixed pixel resolution, face recognition, pattern recognition, etc. whereby entropy can be simply calculated in any other band or according to its standard definition and hence feature extraction can be made.

Biogeography and Plate Tectonics Based Optimization for Water Body Extraction in Satellite Images

Recent advances in remote sensing have widened the platform for research in science and technology. Undoubtedly the estimation of geo-bio-physical properties of the land cover features like water, urban, vegetation, rocky and barren areas play an important role in environmental, transportation and region planning, natural disaster, industrial and agricultural production. Since the water transport is cheapest, extraction of the water body in hyper spectral images of remote areas for which we don’t have enough details of its terrain is inevitable. Till now, natural computation and bio-inspired intelligent techniques like DNA computing, membrane computing, genetic algorithms, neural computing have been used for demonstrating the applications of computational intelligence in the field of remote sensing. However, geo-science has never been used as a nature inspired intelligent technique for developing a computational model. This paper demonstrates the evolution of a new geo-science based approach for satellite image processing using an analogy between plate tectonics and biogeography based optimization. The paper presents biogeography and plate tectonics based optimization (BPBO) as a powerful paradigm for identifying water body area in the satellite image and hence, make a significant contribution towards the development of a new computational intelligence technique in the field of AI. Our major assumption is that it is the entropy which is the driving force leading to the formation of heterogeneous regions called as plates, similar to the convection force in the mantle of the Earth.

Hybridization of gravitational search algorithm and biogeography based optimization and its application on grid scheduling problem

The Gravitational Search Algorithm (GSA) is a nature inspired optimization algorithm which is based on Newtons law of gravity and law of motion. Biogeography Based Optimization (BBO) is also another nature inspired optimization algorithm based on the concept of biogeography (migration and mutation among population). Both of these optimization technique are population based and individually have been applied to a large number of areas. In this paper, we are providing a hybrid GSABBO algorithm that will use the best properties of both the algorithm to enhance the exploration and exploitation properties and reach at the global optimal solution. Grid Computing refers to the sharing of resources across multiple domains to achieve a common goal. Sharing of the resources within an organization helps to enhance its overall performance computationally and economically. The advantages derived from Grid Computing are largely dependent on the scheduling algorithm we use to schedule various jobs across various resources available. This paper introduces a new approach based on the hybridization of BBO and GSA to generate optimal schedules to complete all the given tasks with minimum make span period.

Enhanced heuristic approach for Travelling Tournament Problem based on extended species abundance models of biogeography

This paper shows a heuristic approach of enhanced simulated annealing based on extended species abundance models of biogeography in order to obtain optimal solution for Travelling Tournament Problem. We upgrade the migration step of BBO by using probabilistic measures and hybridize it with simulated annealing to solve the TTP problem and avoid the problem of local minima. Our proposed hybrid approach converges to an optimal solution for TTP. There is negative impact of non deterministic problems on the TTP solution. We considered all these non-deterministic problems as noise. The physical significance of noise in our algorithm is any existing parameter which can affect the fitness of the habitat. We also calculate the overall cost of TTP for various extended species abundance models of BBO (Linear and Non linear models) to achieve desirable results. We compare the performance of our approach with other methodologies like ACO and PSO.

A hybrid biogeography based heuristic for the mirrored traveling tournament problem

The Travelling Tournament Problem (TTP) is a NP-hard problem that abstracts the features of certain types of sports scheduling problems. Objective of TTP is to minimize the total distance travelled by all the teams in a sports league during the tournament. This work proposes a novel hybrid approach of Biogeography Based optimization (BBO) and Simulated Annealing (SA) heuristics to generate schedules for mirrored version of TTP. We evaluate our hybrid algorithm on publicly available standard benchmarks. Results are found to be competitive with existing techniques in literature while taking reasonable amount of time.

Biogeography based anticipatory computing framework for intelligent battle field planning

It is possible to go and physically observe any situation under the area of our reach, but this is not so for the areas beyond our physical boundaries. For the purpose, a methodology inspired from nature is proposed using remote sensing inputs based on swarm intelligence for the anticipatory computation of the regions beyond our borders. The paper presents a nature inspired anticipatory computing framework for intelligent preparation of the battlefield. The algorithm predicts the most suitable destination for the enemy troops to position their forces, for which it uses the population based optimization technique i.e. Biogeography Based Optimization. The paper also introduces a new concept of efforts required in migration to a high HSI solution for optimization in BBO and hence also proposes an advanced optimization technique that was originally proposed by Dan Simon in December, 2008 [5]. Hence, the algorithm can be used to improve the Ant Colony Optimization (ACO) approach, since it lacks the ability to predict the destination and can only find a suitable path to the given destination, leading to coordination problems and target misidentification which can lead to severe casualties. The algorithm can be of major use for the commanders in the battlefield who have been using traditional decision making techniques of limited accuracy for predicting the destination.