Land Use and Land Cover Classification Using Deep Learning Techniques

abstract: Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification. The benefits of automatic visible band VHR LULC classifications may include applications such as automatic change detection or mapping. Recent work has shown the potential of Deep Learning approaches for land use classification; however, this thesis improves on the state-of-the-art by applying additional dataset augmenting approaches that are well suited for geospatial data. Furthermore, the generalizability of the classifiers is tested by extensively evaluating the classifiers on unseen datasets and we present the accuracy levels of the classifier in order to show that the results actually generalize beyond the small benchmarks used in training. Deep networks have many parameters, and therefore they are often built with very large sets of labeled data. Suitably large datasets for LULC are not easy to come by, but techniques such as refinement learning allow networks trained for one task to be retrained to perform another recognition task. Contributions of this thesis include demonstrating that deep networks trained for image recognition in one task (ImageNet) can be efficiently transferred to remote sensing applications and perform as well or better than manually crafted classifiers without requiring massive training data sets. This is demonstrated on the UC Merced dataset, where 96% mean accuracy is achieved using a CNN (Convolutional Neural Network) and 5-fold cross validation. These results are further tested on unrelated VHR images at the same resolution as the training set.Dissertation/ThesisMasters Thesis Computer Science 201

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.Comment: This manuscript is the accepted version for Proceedings of the IEE

Automatska klasifikacija slika zasnovana na fuziji deskriptora i nadgledanom mašinskom učenju

This thesis investigates possibilities for fusion, i.e. combining of different types of image descriptors, in order to improve accuracy and efficiency of image classification. Broad range of techniques for fusion of color and texture descriptors were analyzed, belonging to two approaches – early fusion and late fusion. Early fusion approach combines descriptors during the extraction phase, while late fusion is based on combining of classification results of independent classifiers. An efficient algorithm for extraction of a compact image descriptor based on early fusion of texture and color information, is proposed in the thesis. Experimental evaluation of the algorithm demonstrated a good compromise between efficiency and accuracy of classification results. Research on the late fusion approach was focused on artificial neural networks and a recently introduced algorithm for extremly fast training of neural networks denoted as Extreme Learning Machines - ELM. Main disadvantages of ELM are insufficient stability and limited accuracy of results. To overcome these problems, a technique for combining results of multiple ELM-s into a single classifier is proposed, based on probability sum rules. The created ensemble of ELM-s has demonstrated significiant improvement of accuracy and stability of results, compared with an individual ELM. In order to additionaly improve classification accuracy, a novel hierarchical method for late fusion of multiple complementary descriptors by using ELM classifiers, is proposed in the thesis. In the first phase of the proposed method, a separate ensemble of ELM classifiers is trained for every single descriptor. In the second phase, an additional ELM-based classifier is introduced to learn the optimal combination of descriptors for every category. This approach enables a system to choose those descriptors which are the most representative for every category. Comparative evaluation over several benchmark datasets, has demonstrated highly accurate classification results, comparable to the state-of-the-art methods