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Abstract. The use of remote sensing images as a source of information in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, identification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users very often prefer to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they are of general purpose, they do not yield satisfying results. This work presents a new interactive approach based on relevance feedback to recognize regions of remote sensing. Relevance feedback is a technique used in content-based image retrieval (CBIR) tasks. Its objective is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with composite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identification of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm.

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Convolutional Neural Networks still suffer from the need for great computational power, oftenrestricting their use on various platforms. Therefore, we propose a new optimization method made for DenseNet, a convolutional neural network that has the characteristic of being completely connected. The objective of the method is to control the generation of the characteristic maps in relation to the moment the network is in, aiming to reduce the size of the network with the minimum of loss in accuracy. This control occurs reducing the number of feature maps through the addition of a new parameter called the Decrease Control or dc value, where the decrease occurs from half of the layers. In order to validate the behavior of the proposed model, experiments were performed using different image bases: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, CALTECH-101, Cats vs Dogs and TinyImageNet. Some of the results achieved were: for the MNIST and Fashion-MNIST base, there was 43% parameter reduction. For the CIFAR-10 base achieved a 44% reduction in network parameters, while in base CIFAR-100 the parameter reduction are 43%. In the CALTECH-101 base the parameter optimization was 35%, while the Cats vs Dogs optimized 30% of model parameters. Finally, the TinyImageNet base was reduced 31% of the parameters.

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