# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 1 Introduction
Inspired by the main workflow of various RNN/2D-CNN-based methods for patch classification [20; 30; 31; 32; 33; 34], the proposed methodology mainly contains four steps: image stacking, patch extraction, 2D-CNN based modelling for classification purposes and post-processing, as depicted in Figure 1.  
Figure 1: The proposed methodology’s high-level scheme: the benefit of using an enhanced 2D-CNN as a classification model alongside with a post-processing stage for map refinement is highlighted. In broad terms, the proposed methodology works as follows: (1) the images are stacked to increase the number of features; (2) the input images are split into patches and fed into the 2D-CNN; (3) the 2D-CNN architecture is designed following a smaller-scale approach, and trained to recognize 10 different types of crops (soybean, corn, cotton, sorghum, non-commercial crops (NCC), pasture, eucalyptus, grass, soil, and Cerrado); and (4) reduce the classification error caused by lower-spatial-resolution images, and a here-introduced post-processing step is performed. Images used in this work are from the Campo Verde database, introduced in [30], which corresponds to a Landsat 8 imagery of a tropical region (Municipality of Campo Verde) from Brazil. Its analysis represents a challenging problem as it holds a wide range of crops, and related research works have mostly been devoted to studying non-tropical areas. As for the design and implementation of the enhanced 2D-CNN, the following architecture is proposed: firstly, the 2D-CNN is trained independently for each one of the sequences by extracting patches of \(32\times 32\times n\) pixels in size, where \(n\) is the number of bands in the sequence. Secondly, the training patches are passed through three layers of convolution, yielding the feature maps. Thirdly, a pooling layer reduces the feature map size. Finally, the classification task itself is carried out by a fully-connected layer and the output layer. As for the post-processing stage, it can be said that it consists in refining the annotations obtained by the 2D-CNN by eliminating discontinuities and misclassified pixels through morphological operators. This post-processing becomes crucial as it makes the the methodology more robust to lower-spatial-resolution images and therefore improves the classification rate. The experimental framework used in this research is designed by following those developed in similar works [30; 31]. Two sequences of the Campo Verde database are considered: 1. from October 2015 to February 2016; and 2. from March to July 2016. The proposed methodology achieves a \(f_{1}\) score of 75.89%, which is higher than the previous results reported in the literature [20; 30; 31; 32; 33; 34]. Indeed, a competitive rise of the classification rate in contrast to benchmark-and-recent works conducting experiments on the same dataset was accomplished. This performance is attributed to the exhaustive search of parameters across all the stages of the proposed methodology (namely, the selection of patches size, and setting of the number of filters for the CNN). For comparison purposes, the ability of an RNN architecture alone to classify patches as a whole while maintaining low computational complexity is also explored. Additionally, a biological technique (iterative label refinement) is also evaluated to make comparisons with the proposed post-processing. This paper is structured as follows: Section 2 presents a brief review of state-of-the-art related works. The database and the methods of the proposed methodology's building blocks are described in Section 3. Section 4 describes the experiments carried out over the Campo Verde database. Section 5 presents the results and discussion across the experiments. Some additional results of evaluating the proposed methodology over urban-materials-related images (Pavia scenes) are presented in Section 6. Finally, Section 7 gathers the concluding remarks.