# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 1 Introduction  
Agriculture is one of the main economic activities in the world. Today, considering the continuous human growth population and the limited food availability, agricultural activities need to be monitored on regular basis, in such a manner that the increase in efficiency in food production is enabled, while protecting the natural ecosystems [1, 2, 3, 4]. In this context, crop classification can be used to provide information about production and thus becomes a useful tool for developing sustainable plans and reducing environmentalissues associated with agriculture [5; 6; 7]. As a result, timely collection and the analysis of data from large crop areas is of great interest. Traditionally, such analysis is carried out by using computational tools and satellite imagery processing with artificial intelligence (AI) techniques [8; 9; 10; 11]. Throughout the years, several AI techniques have been explored to tackle the problems related with crop classification [12; 13]. In such vein, machine learning (ML) benchmark algorithms have been successfully used from both unsupervised [14; 15] and supervised [16] inferences. Nonetheless, conventional ML techniques may not be recommendable (and may even be prohibitive) when a manual feature extraction stage is unfeasible. In addition, ML approaches may require exhaustive parameter tuning to reach a high accuracy. In this connection, by overcoming these drawbacks, deep learning (DL) has recently taken place as one of the most appealing approaches. Broadly, DL approaches can be divided into artificial neural networks (ANNs), recurrent neural network (RNNs) and convolutional neural networks (CNNs) [17]. Particularly, for image classification problems, CNNs and RNNs are preferred over conventional ANNs as they extract sequential information. RNNs have proven to be effective at extracting temporal correlations and classifying data as a whole while maintaining a manageable computational complexity. In this regard, some works [18; 19] have proposed novel network architectures based on RNNs combined with CNNs for automated feature extraction from multiple satellite images through learning time correlation. CNNs are of special interest as their main advantage over other techniques--besides automatically extracting features through convolutional layers--is their ability to capture spatial features (i.e., the pixel arrangement and relationships thereof) [20; 21] as well as its versatility [22; 23]. Recent works based on hybrid methods combining 2D- and 3D-CNN [24] have proven that 3D-CNNs enable the joint spatial-spectral feature representation from stacked spatial bands. Such methods have been shown to be less computationally expensive than those solely based on 3D-CNN architectures [25; 26; 27]. In addition, some exploratory studies exhibit that 3D-CNNs may underperform 2D-CNNs when classes have similar textures across multiple spectral bands [28], and therefore 2D-CNN-based approaches are preferred by the vast majority of recent research works on crop classification. Following from these insights and given that this work is not intended to include spatial-temporal information, 2D-CNNs are the architectures of choice for this study. Nonetheless, despite being appealing for image analysis in agriculture settings, techniques based on 2D-CNNs may involve complex architectures with a great amount of layers and parameters entailing a high computational cost and training time [25; 29]. Therefore, there is still a need for creating more accurate crop classification systems which involve a lower computational burden. Aimed at establishing a way forward to provide solutions in this regard, this work introduces a competitive methodology for crop classification from multispectral satellite imagery by taking advantage of using an enhanced 2D-CNN together with a novel post-processing step.