A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

Abstract

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics.

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

“…CNN-Polygon also resulted in the highest performance when the sample size was less than 200 for Gwangju. This implies that the graph-based CNNs can yield successful classification results with a small training sample size, unlike recent CNN-based land cover classification studies that used large datasets with hundreds to thousands of training samples per class [18,19,31,36,64]. As the sample size increased, the performance of matrix-based models (i.e., CNN-Matrix and CNN-1D) increased to similar or slightly higher levels than the graph image-based models.…”

“…The Wilcoxon paired signed-rank test was used only when two models were compared [60][61][62]. The Friedman test was used for the multiple model comparisons [63][64][65] since multiple The first and second rows show the rate of occurrence of line and polygon graphs as density using the reference data. An area with a high occurrence rate means that the majority of graphs were plotted over the area.…”

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

“…Radiometric calibration and atmospheric correction, which will eliminate systematic errors introduced by system [36] and atmosphere, respectively, were applied to the hyperspectral raw data. While radiometric calibration is performed by the data provider, atmospheric correction is generally performed by the user.…”

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

“…When CNNs are used for pixel classification, the accuracy is high in the inner area but low in the edge area, resulting in rough edges [60,61]. Because the rough edges are caused by the differences in feature values between pixels of the same type, it is necessary to introduce appropriate post-processing methods to improve the accuracy of edge pixel classification [62][63][64].…”