Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Abstract

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

“…Hence, we decided to conduct a manual digitizing in order to obtain the best possible segmentation on our objects of interest. This was already done in a similar way by Aguilar et al [10]. Therefore, 694 reference objects provided by Vicasol were manually digitized over the …”

“…All of these aforementioned works were performed by using different pixel-based approaches. Recently, the object-based image analysis (OBIA) approach has been applied to greenhouse detection from digital true color (RGB) aerial data [9] and WorldView-2 and GeoEye-1 stereo pairs [10]. However, and to date, no investigation has been aimed at the identification of horticultural crops growing underneath greenhouse coverings.…”

AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery

“…AssesSeg was used to detect the best band combinations for the detection of plastic-covered greenhouses from three multispectral satellite data: Sentinel-2 (S2), Landsat 8 (L8), and WorldView-2 (WV2) images. The authors' choice to test AssesSeg on plastic-covered greenhouses segmentation was due to their sound experience on this active research topic using both pixel-based approaches [5][6][7][8] and OBIA approaches [9][10][11][12][13][14]. The performed test also demonstrates the importance of the number of reference objects in segmentation quality assessment.…”

Aerial thermography from low-cost UAV for the generation of thermographic digital terrain models

“…Regarding the use of thermographic cameras, some studies have approached the use of UAVs for the geo− metric -thermographic inspection of façades, acquiring data difficult to reach from a terrestrial point of view, but missing important information of the roof [16]. What is more, multispectral cameras have been used for agricultural purposes, [17][18], such as monitoring the application of herbicide in turf grass and detection and classification of greenhouses.…”