# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 2 Related Works  
Satellite and aerial images have been classified with several techniques, including ML and DL methods for different purposes. A recent survey about different techniques used in remote sensing [17] reports that DL architectures is one of the most used techniques for farming applications. Therefore, remarkable studies on crop classification from images have been devoted to this kind of technique. The authors in [35] carried out a study on remotely sensed time series in California in order to classify 13 summer crop categories. The authors compared traditional methods such as random forest and support vector machine (SVM) with two DL architectures: a long short-term memory (LSTM) and a 1D-CNN, demonstrating that the 1D-CNN architecture reaches the best results (an accuracy around 85.54%) among all DL and non-deep learning models. Similarly, Ref. [36] is devoted to classify 14 types of crops in Sentinel images along 254 hectares of land in Denmark. It used a DL architecture inspired by the results from the combination of two networks, a fully connected network (FCN) and an RNN. The latter architecture was also used by [37] for classifying crops in Sentinel images of France; the classified crops are rice, sunflower, lawn, irrigated grassland, durum wheat, alfalfa, tomato, melon, clover, swamps, and vineyard. The proposed approach is compared against traditional methods such as the \(k\)-nearest neighbor (\(k\)-NN) and SVM, exhibiting better results when using the DL approach. The work carried out in [38] classifies 22 different crops of aerial images using a CNN with local histograms. The method extracts information related with texture patterns and color distribution to achieve scores of 90%. The study in [18] combines a CNN and an RNN in a pixel-based approach to classify 15 types of crops on multi-temporal Sentinel-2 imagery of Italy. The DL method was compared with ML methods including SVM and RF. The best accuracy values were reached by the R-CNN approach with a value of 96.5%. A novel classification technique is proposed in [19], which applies transfer learning (TL) to solve the problem related to imbalanced databases. It was tested on a crop database with the aim of recognizing pests. Furthermore, this research compares various CNN architectures and achieves an accuracy over 95%. Another work [39] uses an RNN-based approach to classify SAR data from China. More specialized studies have explored the benefit of using 3D-CNNs in spatio-temporal remote sensing images. For instance, in [25], a new paradigm for crop identification area by using 3D-CNNs is introduced to incorporate the dynamics of crop growth. Likewise, the research presented in [26] used a 3D-CNN to classify four scenes where urban areas as well as crops (including lettuce and corn) could be found. The proposed 3D-CNN together with a principal component analysis (PCA) stage (applied to extract the most important information from the images) achieves an overall accuracy above 95%. Another 3D-CNN model for cloud classification was explored in [27], which was tested over two databases (GF-1 WFV validation data and ZY-3 validation data). Such a model reaches an accuracy of 97.27%. The work developed in [40] classifies a tree database of Finland. There are 4142 trees clustered into three classes (pine, spruce and birch). The classification stage was carried out with four convolutional layers and three max pooling layers, accomplishing an accuracy of approximately 94%. A very recent work in the field of hyperspectral image analysis using 3D-CNN [41], similarly to [40], mainly aimed to classifying trees from a Finnish database. It compared both DL and ML methods, and experimentally demonstrated that 3D-CNN yields the best results (above 91% of accuracy). A 3D-CNN is also successfully used to classify soybean and grass from the MODIS data of USA [42]. The preliminary results of a 3D-CNN classification model of cotton and corn crops from the Sentinel data of Turkey are outlined in [43].