Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

Abstract

The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.

Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

“…In their review, Ma et al [26] showed that nearly 200 publications using deep convolutional neural networks (CNNs) have been published in the field of remote sensing by early 2019 of which most focused on land use land cover (LULC) classification [28], urban feature extraction [29][30][31], and crop detection [32,33]. Deep learning approaches often require a large amount of training data, and there are benchmark datasets publicly available for training and testing of deep learning approaches in the abovementioned remote sensing fields.…”

Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net

“…In this paper we have presented an automated approach to mapping banana plantations using the U-Net CNN architecture [33] to assist the biosecurity response to Foc TR4. The U-Net has been successfully applied to other land uses around the world, but not at an operational level [40,54,55]. Until this study, there were no existing automated classifications to detect banana plantations using high-resolution aerial photography in Queensland, Australia or globally.…”

“…Most studies that have used deep learning to automatically map land use features have a constrained geographical extent and are limited to a standard set of training images, for example the University of California Merced Land Use Dataset [38] and Banja-Luka [39]. Although this is advantageous in benchmarking different methodologies, no studies have operationalized the applications for real-world land use mapping over a large geographical area [23,40].…”

Tree, Shrub, and Grass Classification Using Only RGB Images

“…For the PASCAL VOC-2012 dataset, DeepLabV3+ has currently the best ranking among several methods including SegNet [24], PSP [25], and FCN [26]. In a very recent study [35], which involves land cover type classification, it was reported that DeepLabV3+ performed better than PSP and SegNet.…”