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Superpixel segmentation showed to be a useful preprocessing step in many computer vision applications. Superpixel’s purpose is to reduce the redundancy in the image and increase efficiency from the point of view of the next processing task. This led to a variety of algorithms to compute superpixel segmentations, each with individual strengths and weaknesses. Many methods for the computation of superpixels were already presented. A drawback of most of these methods is their high computational complexity and hence high computational time consumption. K mean based SLIC method shows better performance as compare to other while evaluating on the bases of under segmentation error and boundary recall, etc parameters.

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From last ten to fifteen years many optimization techniques have been evolved and enhanced. Out of those, PSO and BBO are the two techniques that have been widely used in Swarm Optimization. PSO is better in comparison many genetic algorithms. PSO has applications in various areas like Optimization, Neural Networks training, Fuzzy controls and etc. BBO is based on science of biogeography. BBO has some features common to PSO and Genetic algorithms but it has some important features that make it more reliable than others. In this paper, we have proposed new algorithm that combines the features of PSO and BBO. It will help in providing more reliability in optimization world.

Abstract-Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised andunsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-ofthe-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. Keywords: remote sensing images, deep learning, image classification I. INTRODUCTIONThe swift advancement in remote sensing image processing area, make the availability of remote data in bulk, in different spatial and spectral resolutions and in dynamic ranges. In recent years, there have been great advances in remote sensing image processing, for both low-level tasks, such as denoising or segmentation, and high level ones, such as classification. Remote sensing image classification is a obligatory step for remote sensing applications like forest management, disaster warning and assessment, military target recognition, thematic mapping, environment monitoring, urban planning. There are a plethora of image classification algorithms have been developed with strong conceptual foundation based on pixels spectral and spatial characteristics like; conventional classifiers, neural networks, machine learning algorithms, genetic algorithms and artificial intelligence. Labeling each pixel of an image to a particular class according to its spectral properties becomes incrementally more challenging as the level of abstraction increases, going from pixel to pixel which is the reason of occurrence of mixed pixels in and image. Classifying mixed pixels is the hardest part to process an remote sensed image because in some cased as high intra class variability then couples with low inter class distance, , a problem that grows ever more as finer classifications are sought, which result the wrong labeling of the pixels. For this problem increasing the resolution is not the solution, theencoding spectral, textural, and geometrical properties, become mostly ineffective. More complex features and descriptors are necessary to capture the semantics of the scene[1], [2], [3]. To improve the results of classification which gives birth to deep learning approach?Deep learning comes from the concept of human brain having multiple types of representation with simpler features at the lower levels and high-level abstractions built on top of that. Humans arrange their ideas and concepts hierarchically. Humans...

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