An Overview of Technological Revolution in Satellite Image Analysis <s> Machine Learning Experts <s> We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Machine Learning Experts <s> We presented a novel procedure to extract ground road networks from airborne LiDAR data. First point clouds were separated into ground and non-ground parts, and ground roads were to be extracted from ground planes. Then, buildings and trees were distinguished in an energy minimization framework after incorporation of two new features. The separation provided supportive information for later road extractions. After that, we designed structure templates to search for roads on ground intensity images, and road widths and orientations were determined by a subsequent voting scheme. This local searching process produced road candidates only, in order to prune false positives and infer undetected roads, a scene-dependent Markov network was constructed to help infer a global road network. Combination of local template fitting and global MRF inference made extracted ground roads more accurate and complete. Finally, we extended developed methods to elevated roads extraction from non-ground points and combined them with the ground road network to formulate a whole network. <s> BIB002 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Machine Learning Experts <s> Abstract The advancement in satellite technology in terms of spatial, temporal, spectral and radiometric resolutions leads, successfully, to more specific and intensified research on agriculture. Automatic assessment of spatio-temporal cropping pattern and extent at multi-scale (community level, regional level and global level) has been a challenge to researchers. This study aims to develop a semi-automated approach using Indian Remote Sensing (IRS) satellite data and associated vegetation indices to extract annual cropping pattern in Muzaffarpur district of Bihar, India at a fine scale (1:50,000). Three vegetation indices (VIs) – NDVI, EVI2 and NDSBVI, were calculated using three seasonal (Kharif, Rabi and Zaid) IRS Resourcesat 2 LISS-III images. Threshold reference values for vegetation and non-vegetation thematic classes were extracted based on 40 training samples over each of the seasonal VI. Using these estimated value range a decision tree was established to classify three seasonal VI stack images which reveals seven different cropping patterns and plantation. In addition, a digitised reference map was also generated from multi-seasonal LISS-III images to check the accuracy of the semi-automatically extracted VI based classified image. The overall accuracies of 86.08%, 83.1% and 83.3% were achieved between reference map and NDVI, EVI2 and NDSBVI, respectively. Plantation was successfully identified in all cases with 96% (NDVI), 95% (EVI2) and 91% (NDSBVI) accuracy. <s> BIB003 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Machine Learning Experts <s> We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. <s> BIB004
The scope of research among ML community emerges with resolving the limitations on statistical models experienced by the remote sensing researchers through enhancing the level of reasoning. The following are the major factors that are considered for resolving the issues on traditional satellite image analysis: • To enrich the reasoning capability of models • To handle huge dimensional data processing in satellite image analysis. • To overcome the limitations of traditional ML classifiers through hybridization or object based classification The satellite images are used in climate based models for monitoring seasons and also hazards like weather forecast, agricultural drought prediction, rainfall prediction, flood mapping etc.-  . The recent innovations in satellite sensor instruments lead to the availability of wide range of satellite images with more increased resolution and dimensionality. Hence, more advanced crop health tracking applications, road network detection, vehicle navigation and urban planning models for cities etc-has also become beneficial in aiding humanity BIB003 BIB002   . P. Sathya et al. (2011) compared the performance of Kmeans clustering and back-propagation neural network for effective image segmentation and classification in satellite images using dense count values of the pixel intensities  . But it would be highly advantageous to use more advanced intelligence schemes like Deep Neural Network (DNN) in satellite image analysis which could bring out improved level of reasoning with reduced training complexities. After 2006, the deep learning schemes are profound in handling complex computations of higher level abstractions  and are moving beyond the capacity of shallow machine learning era. The complexities on training the deep networks with huge number of hidden layers are resolved to a greater extent by a greedy learning based algorithm BIB001 . These DNN learning schemes are more vibrant now, in the field of speech, natural language processing and internet technologies BIB004 . The following sections 3 and 4 portray the works of both the research communities to exploit the level of intelligence through statistical and ML based methods for satellite image interpretation models.