Evaluation of CNNs for land cover classification in high-resolution airborne images

Gisela Häufel; Lukas Lucks; Melanie Pohl; Dimitri Bulatov; Hendrik Schilling

doi:10.1117/12.2325604

9 October 2018 Evaluation of CNNs for land cover classification in high-resolution airborne images

Gisela Häufel, Lukas Lucks, Melanie Pohl, Dimitri Bulatov, Hendrik Schilling

Proceedings Volume 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX; 1079003 (2018) https://doi.org/10.1117/12.2325604
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

Semantic land cover classification of satellite images or airborne images is becoming increasingly important for applications like urban planning, road net analysis or environmental monitoring. Sensor orientations or varying illumination make classification challenging. Depending on image source and classification task, it is not always easy to name the most discriminative features for a successful performance. To avoid feature selection, we transfer aspects of a feature-based classification approach to Convolutional Neural Networks (CNNs) which internally generate specific features. As land covering classes, we focus on buildings, roads, low (grass) and high vegetation (trees). Two different approaches will be analyzed: The first approach, using InceptionResNetV2, stems from networks used for image recognition. The second approach constitutes a fully convolutional neural network (DeepLabV3+) and is typically used for semantic image segmentation. Before processing, the image needs to be subdivided into tiles. This is necessary to make the data processible for the CNN, as well as for computational reasons. The tiles working with InceptionResNetV2 are derived from a superpixel segmentation. The tiles for working with DeepLabV3+ are overlapping tiles of a certain size. The advantages of this CNN is that its architecture enables to up-sample the classification result automatically and to produce a pixelwise labeling of the image content. As evaluation data for both approaches, we used the ISPRS benchmark of the city Vaihingen, Germany, containing true orthophotos and ground truth labeled for classification.

Citation Download Citation

Gisela Häufel, Lukas Lucks, Melanie Pohl, Dimitri Bulatov, and Hendrik Schilling "Evaluation of CNNs for land cover classification in high-resolution airborne images", Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 1079003 (9 October 2018); https://doi.org/10.1117/12.2325604

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available