Mapping Plastic Greenhouses Using Spectral Metrics Derived From GaoFen-2 Satellite Data

Abstract

Plastic greenhouses are an important hallmark of agricultural progress. To meet the growing demand for vegetable and food, the amount of plastic greenhouses has increased significantly over the past few decades. Remote sensing is considered as a promising data source for taking inventory and monitoring plastic greenhouses for managing modern agriculture. However, a systematic catalog of number and spatial distribution of plastic greenhouses is mostly inexistent. This is primarily due to the complex land surface characteristics and seasonal changes, which make automated classification based on EO data challenging. Current approaches generally suffer from the susceptibility of approaches toward thresholds and changes in the phenological stage. Besides, they often require an extensive training of models, however, often the necessary amount of training data is inexistent. To address these issues, we suggest an adaptable and universal plastic greenhouse mapping method based on very high spatial resolution optical satellite data (GaoFen-2 image) with a threestep procedure. A plastic greenhouse gathering area (100 km 2) is selected for the development of the initial method. We receive a very competitive mapping accuracy 97.34% and the likelihood of plastic greenhouses being mapped correctly reaches to 95.20%. Subsequently, we transfer it to a much larger area (2025 km 2) featuring a different phenological stage and different surrounding patterns. The stable mapping accuracy proves the validity of our approach.

Few-Shot Object Detection With Self-Adaptive Attention Network for Remote Sensing Images

“…where is the sigmoid activate function and [, ] denotes to concatenate two tensors. 1 is a weight matrix to be learned and 47+ is previous hidden state which has the same dimension compared with input 4 . In a similar way, the update gate is computed by…”

“…HANKS to the development recently in computer vision, the past few decades have seen the rapid progress in remote sensing technology which has brought quantities of applications [1]- [4], such as environmental management, forecasts of disasters, and assistance in rescue operations. Development of VHR (very high resolution) remote sensing images provide us with more detailed geo-spatial objects information including diversities in scale, orientation, and shape.…”

Optimal spatial resolution of remote-sensing imagery for monitoring cantaloupe greenhouses

“…Wu et al [10] reported that, when using a single-texture algorithm to identify plastic greenhouses, the recognition accuracy of a local binary model exceeds that of the gray-level co-incidence matrix and the pixel shape index algorithm. Shi et al [13] proposed a three-step stratified model to extract plastic greenhouses based on GF-2 remote-sensing data; that is, they first distinguished plastic greenhouses, vegetation, and other feature types by using the double-coefficient vegetation screening index. Next, they used the high-density vegetation suppression index to screen out high-density vegetation features.…”

Object-Based Classification of Greenhouses Using Sentinel-2 MSI and SPOT-7 Images: A Case Study from Anamur (Mersin), Turkey

“…Greenhouse classification research works have been performed by using numerous pixel-based and object-based classification methods with several remote sensing data, such as Landsat Thematic Mapper [7], Landsat-8 Operational Land Imagery (OLI) [8], the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer, IKONOS, QuickBird [9], WorldView-2 (WV2) [10], [11], GaoFen-2 [12], and Sentinel-1 and Sentinel-2 MultiSpectral Instruments (MSI) [5].…”

“…It was also stated that textural information shows a positive effect on PML classification with SVM and RF classifiers. Shi et al [12] proposed a plastic greenhouse mapping method based on the GaoFen-2 image with a three-step procedure. They reached an OA of 97.34%.…”