An FCN takes the input of any size and produces fixed-size output with effective training and interpretation. The confusion (error) matrix is the frequently used classification accuracy and uncertainty method [21]. These features are developed with the features of a bottom-up approach through adjacent connections of the network. The intent of the classification process is to categorize all pixels in a digital image into one of several land cover classes, or "themes". The network accepts input data image and produces a set of rectangular object proposals as output with an objectness score. Layer S4 consists of a 5×5 feature map connected to a 2×2 neighborhood and has 32 parameters with 2000 connections between neurons. From the perspective of the computer vision practitioner, there were two steps to be followed: feature design and learning algorithm design, both of which were largely independent. The higher layers' locations are related to the image locations and connected to receptive fields. We aimed for the best result in the image handling field. The image classification problem requires determining the category (class) that an image belongs to. Extensive studies using LBP descriptor have been carried out in diverse fields involving image analysis [10–12]. This property was considered to be very important, and this lead to the development of the first deep learning models. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. CUDA is NVIDIA's [26] parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU (graphics processing unit). The ResNeXt network is built by iterating a building block that combines a group of conversions within a similar topology. There are already a big number of models that were trained by professionals with a huge amount of data and computational power. In the deep learning technique, a several number of models are available such as convolutional neural network (CNN), deep autoencoders, deep belief network (DBN), recurrent neural network (RNN), and long short-term memory (LSTM). CNN architecture of Faster R-CNN. Previous studies mostly rely on manual work in selecting training and validation data. The classification layer has 2000 scores that evaluate the probability of an object, and the regression layer has 4000 output coordinates of k anchor boxes. Agreement with ground truth measured with quadratic weighted Cohen's kappa or Spearman's correlation coefficient for breast cancer grading (TUPAC16). The first phase generates class-independent proposal regions. Not all of them fulfill the invariances and insensitivity of ideal features. After all the waiting specimens are classified through by the classifiers, where the specimen belongs will be decided through voting. The semantic-level image classification aims to provide the label for each scene image with a specific semantic class. This results in generating an output at the end of the network that has the original image size; see Fig. forest) may contain a number of spectral sub-classes with unique spectral variations. The RPN method performs object detection in a different sort of scales and aspect ratios. Fully convolutional networks (FCNs) [9] are a deep as well as influential architecture in semantic segmentation. This optimized constraint decreases the selection of hyper-parameters. Spectral classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the different spectral channels of the data. The workflow involves multiple steps to progress from preprocessing to segmentation, training sample selection, training, classifying, and assessing accuracy. An enhancement would not add anything useful, as far as the classification algorithm is concerned. 1. The era of AI democratizationis already here. In real-time applications, the unsupervised feature learning methods have achieved high performance for classification compared with handcrafted-feature learning methods [9]. patents-wipo. The categorization law can be devised using one or more spectral or textural characteristics. Region-based fully convolutional neural networks (R-FCN) [16] architecture is used for object detection. The method uses a new dimension called cardinality that defines the size of transformations in addition to width and depth. Every hidden layer is processed with the rectification nonlinear activation function. The selection of appropriate training areas is based on the analyst's familiarity with the geographical area and their knowledge of the actual surface cover types present in the image. To evaluate the activation function of ConvNet, the value zero is assigned to all other activations. R-FCN accepts the given input data image and is able to classify RoIs into object classification and background. However, parallel programming has been developed as a powerful, general purpose and fully programmable parallel data processing approach for operations that require it. From the results of the experiments on the CIFAR dataset, we argue that the network depth is of the first priority for improving the accuracy. The accuracy of the training shows that it is not easy to optimize a deeper network. Faster R-CNN is also used for multi-scale anchors for sharing the information without any additional cost. In yet another work [29], authors applied MKL-based feature combination for identifying images of different categories of food. Initially feature extraction techniques are used to obtain visual features from image data and second step is to use machine intelligence algorithms that use these features and classify images into defined groups or classes. SegNet addresses this issue by tracking the indices of max-pooling, and uses these indices during unpooling to maintain boundaries. After that, input from the subsampling layer is forwarded through a pooling layer to reduce the number of parameters. Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. LeNet 5 architecture is useful for handwriting, face, and online handwriting recognition, as well as machine-printed character recognition. The dropout technique is used to reduce the number of parameters. Efforts to scale these algorithms on larger datasets culminated in 2012 during the ILSVRC competition [79], which involved, among other things, the task of classifying an image into one of thousand categories. So we need to improve the classification performance and to extract powerful discriminant features for improving classification performance. The DeconvNet perform filtering and pooling in reverse order of ConvNet. Deep neural networks naturally combine low-level, middle-level, and high-level features in a multi-stage pipeline and deepened by the depth of the layers. The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images. One of the most effective innovations in the architecture of convolutional neural networks, and also award-winning, is AlexNet [3] architecture. https://gisgeography.com/image-classification-techniques-remote-sensing Because classification results are the basis for many environmental and socioeconomic applications, scientists and practitioners have made great efforts in developing advanced classification approaches and techniques for improving classification accuracy. Remotely sensed raster data provides a lot of information, but accessing that information can be difficult. The main objective of feature pyramid networks (FPN) [18] is to build the feature pyramids with minimum cost. The first module identifies the object proposals, and the second uses the object proposals for detection. In DeconvNet, unpooling is applied; rectification and filtering are used to restructure the input data image. ZFNet has eight layers, including five convolutional layers that are associated with three fully connected layers. LBP initially proposed in [10] is one of the prominent and most widely used visual descriptors because of its low computational complexity and ability to encode both local and global feature information. Once the computer has determined the signatures for each class, each pixel in the image is compared to these signatures and labeled as the class it most closely "resembles" digitally. A comparison of CNN methods is shown in Table 5.1. Many state-of-the-art learning algorithms have used image texture features as image descriptors. patents-wipo. Earlier, the spatial satellite image resolution was used, which was very low, and the pixel sizes were typically coarser and the image analysis methods for remote sensing images are based on pixel-based analysis or subpixel analysis for this conversion [2]. A conservative solution for this issue is to use unsupervised learning followed by a supervised training. For object recognition, local features and bag of visual features from medical images have also been used quite successfully [6,8,9]. ted2019. However, depending on the classification task and the expected geometry of the objects, features can be wisely selected. Classifiers such as decision trees [19], nearest neighbor [5,20], and kernel-based SVMs [16,21] have been used in medical image analysis. Chen Houqun, ... Dang Faning, in Seismic Safety of High Arch Dams, 2016, Support vector machine image classification. Unlike mitosis detection, image classification challenges have used different evaluation approaches for ranking the participant algorithms. Finally, the output is produced from three fully connected layers. 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