Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms

Abstract

Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss prevention and global food security. However, precise and continuous spectral signatures, important for large-area crop growth monitoring and early prediction of yield production with cutting-edge algorithms, can be only provided via hyperspectral imaging. Therefore, this article used new-generation Deutsches Zentrum für Luft-und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) images to classify the main crop types (hybrid corn, soybean, sunflower, and winter wheat) in Mezőhegyes (southeastern Hungary). A Wavelet-attention convolutional neural network (WA-CNN), random forest and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands. The best accuracy was achieved with the WA-CNN, a feature-based deep learning algorithm and a combination of two images with overall accuracy (OA) value of 97.89%and the user's accuracy producer's accuracy was from 97% to 99%. To obtain this, first, factor analysis was introduced to decrease the size of the hyperspectral image data cube. A wavelet transform was applied to extract important features and combined with the spectral attention mechanism CNN to gain higher accuracy in mapping crop types. Followed by SVM algorithm reported OA of 87.79%, with the producer's and user's accuracies of its classes ranging from 79.62% to 96.48% and from 79.63% to 95.73%, respectively. These results demonstrate the potentiality of DESIS data to observe the growth of different crop types and predict the harvest volume, which is crucial for farmers, smallholders, and decision-makers.

A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

“…When using SVM, the main parameters that need to be modified are the Gamma (G) value and the Cost (C) parameter. Based on the proven performance in the previous articles, the kernel function was set to 'Radial Basis Function' (RBF) [71].…”

EXNet: (2+1)D Extreme Xception Net for Hyperspectral Image Classification

“…Deep CNNs can be prone to overfitting, and for such reasons, regularization techniques are often required to mitigate this issue [26], [39]- [42]. Training Deep CNNs is computationally expensive, and the time required can be prohibitive, especially for large datasets [43]. Selecting an appropriate CNN architecture for HSIC is a non-trivial task.…”

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

“…With an improvement of higher-resolution RSIs, the necessities of classification are always attained higher, the classification of complex scenes and the needs of rotation translation invariance provide a begin to several researches on deep learning (DL) in the domain of RS Land cover and crop category maps are major necessary inputs while handling with agricultural and environmental monitoring tasks ( Bouguettaya et al, 2023 ). Multi-temporal multisource satellite images are commonly needed for capturing particular crop development phases and therefore, capable of discriminating various kinds of crops ( Farmonov et al, 2023 ). For instance, multispectral optical images may not be sufficient for separating summertime crops in a heterogeneous and complex environment.…”