An Overview of Technological Revolution in Satellite Image Analysis <s> Hybrid methods <s> Abstract Various experimental comparisons of algorithms for supervised classification of remote-sensing images have been reported in the literature. Among others, a comparison of neural and statistical classifiers has previously been made by the authors in (Serpico, S.B., Bruzzone, L., Roli, F., 1996. Pattern Recognition Letters 17, 1331–1341). Results of reported experiments have clearly shown that the superiority of one algorithm over another cannot be claimed. In addition, they have pointed out that statistical and neural algorithms often require expensive design phases to attain high classification accuracy. In this paper, the combination of neural and statistical algorithms is proposed as a method to obtain high accuracy values after much shorter design phases and to improve the accuracy–rejection tradeoff over those allowed by single algorithms. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Hybrid methods <s> The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article. A support vector machine (SVM) is considered for classifying the pixels into different landcover types. It is initially designed using a small set of labeled points, and subsequently refined by actively querying for the labels of pixels from a pool of unlabeled data. The label of the most interesting/ ambiguous unlabeled point is queried at each step. Here, active learning is exploited to minimize the number of labeled data used by the SVM classifier by several orders. These features are demonstrated on an IRS-1A four band multi-spectral image. Comparison with related methods is made in terms of number of data points used, computational time and a cluster quality measure. <s> BIB002 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Hybrid methods <s> In this paper a new approach to performing change detection analyses based on a combination of supervised and unsupervised techniques is presented. Two remotely sensed, independently classified images are compared. The change estimation is performed according to the Post Classification Comparison (PCC) method if the posterior probability values are sufficiently high; otherwise a land cover transition matrix, automatically obtained from data, is used. The proposed technique is compared with the traditional PCC approach. It is shown that the new approach correctly detects the ''true change'' without overestimating the ''false'' one, while PCC points out ''true change'' pixels together with a large number of ''false changes''. <s> BIB003 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Hybrid methods <s> The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms. The image segmentation based on the clustering can be formulated as an optimization problem.We design a novel multiobjective optimization to solve the image segmentation.The multi-state quantum bits are used to represent individuals.A simple heuristic method is adopted to select a preferred solution from the final Pareto front.Our proposed method is tested on one simulated SAR image and two real SAR images. <s> BIB004 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Hybrid methods <s> A modified ABC algorithm based fast satellite image segmentation has been presented.ABC, PSO and GA methods are compared with this proposed method.The experimental results demonstrate better performance of MABC based technique.The proposed MABC based approach is much faster (CPU time is less).The validity of the proposed technique is reported both qualitatively and quantitatively. In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized. <s> BIB005
The hybridization of different machine learning methods and the integration of statistical and machine learning methods could acquire more efficient results. Giorgio BIB001 proposed an approach with combination of neural and statistical algorithms in a simple design phase to clearly reveal that every algorithm is significant in solving a specific issue and no single algorithm is proved to be perfect BIB001 . . Castellana et al., (2007) performed the combination of both supervised and unsupervised mode of change detection on pixel based method to achieve better classification of remote sensing images BIB003 . Pabitra Mitra et al., (2004) combined active learning method with Support Vector Machine (SVM) and developed an active SVM with reduced labeled data for classification BIB002 . Qihao Weng et al., (2004) combined Maximum likelihood and Decision Tree classifier to estimate the vegetation abundance in a land cover considering the Land Surface Temperature (LST) and vegetation relationship  . Few more hybrid models for satellite image interpretation have been tabulated below in the following Tab. 2. Kazim Hanbay et al., (2014) have proposed a novel image segmentation algorithm for Synthetic Aperture Radar images using the combination of improved Artificial Bee Colony (ABC) algorithm and Neutrosophic set. Here, the hybrid feature extraction model using neutrosophic and cooccurrence matrices achieved effective localization of edges in images  . Similarly, A.K. BIB005 proposed a Modified ABC (MABC) algorithm for satellite image segmentation with very less control parameters and on applying multi-level thresholding functions like Kapur's, Otsu and Tsallis objective function BIB005 .The Genetic Algorithm (GA) is used in automatic feature selection methods since it is well known for high level of optimization in refining the irrelevant and noisy features. The fuzzy algorithms stand high in handling the level of uncertainty. Hence, the fusion of these approaches could enhance the level of classification accuracy in ML models. Other than this, novel ideas from natural heuristics, human anatomy and molecular reactions are also imparted in evolutionary algorithms for improving the performance of existing ML methods. The limitations of traditional clustering algorithms on slower convergence speed and effects on wrong initialization parameters are resolved using the Quantum inspired Multi-objective Evolutionary Clustering (QMEC) algorithm which is based on the quantum principle of energy interactions BIB004 .