Qikai Lu

New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study

This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications.

A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery

In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. The clustering approach is able to group the high-dimensional data into a subspace by mining the salient information and suppressing the redundant information. In this way, the relationship between neighboring pixels, which was hidden in the original data, can be extracted more effectively. Specifically, in the proposed algorithm, a two-step process is adopted to make use of the clustering-based information. A clustering approach is first used to produce the initial clustering map, and, subsequently, a multiscale cluster histogram (MCH) is proposed to represent the spatial information around each pixel. In order to evaluate the robustness of the proposed MCH, four clustering techniques are employed to analyze the influence of the clustering methods. Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM), the 3D wavelet texture, and differential morphological profiles (DMPs). The experiments conducted on four well-known hyperspectral datasets verify that the proposed MCH can significantly improve the classification accuracy, and it outperforms other commonly used spatial features.

A Novel MRF-Based Multifeature Fusion for Classification of Remote Sensing Images

The spatial information has been proved to be effective in improving the performance of spectral-based classification. However, it is difficult to describe different image scenes by using monofeature owing to complexity of the geospatial scenes. In this letter, a novel framework is developed to combine the multiple spectral and spatial features based on the Markov random field (MRF). Specifically, the pixels in an image are separated into reliable and unreliable ones according to the decision of multifeature classifications. The labels of the reliable pixels can be conveniently determined, but the unreliable pixels are then classified by fusing the multifeature classification results and reducing the classification uncertainties based on the MRF optimization. Experiments are conducted on three multispectral high-resolution images to verify the effectiveness of the proposed method. Several state-of-the-art multifeature classification methods are also achieved for the purpose of comparison. Moreover, three classifiers (i.e., multinomial logistic regression, support vector machines, and random forest) are used to test the performance of the proposed framework. It is shown that the proposed method can effectively integrate multiple features, yield promising results, and outperform other approaches compared.

Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas

Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS) data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples.

A structural similarity-based label-smoothing algorithm for the post-processing of land-cover classification

ABSTRACT Post-processing is able to achieve a satisfactory classification performance with a low cost and simple assumption, making it widely used in the refinement of classification maps. In this study, a novel structural similarity-based label-smoothing algorithm is developed for the post-processing of land-cover classification. Inspired by the non-local (NL) means algorithm, the proposed algorithm assigns different voting weights to the neighbouring pixels for the identification of the central pixel. Here, the voting weight of a specific neighbouring pixel depends on its structural similarity to the central pixel. In this paper, two measurements are proposed to evaluate the similarity between pixels: (1) a consistency criterion; and (2) a histogram similarity criterion. The proposed algorithm was tested on three remote-sensing images. The experimental results confirm that the proposed algorithm reduces the classification noise and preserves the detail and structural information at the same time. Compared to the traditional post-processing approaches (e.g., majority voting), the proposed algorithm exhibits a more satisfactory performance.

Retrieving Aerial Scene Images with Learned Deep Image-Sketch Features

This paper investigates the problem of retrieving aerial scene images by using semantic sketches, since the state-of-the-art retrieval systems turn out to be invalid when there is no exemplar query aerial image available. However, due to the complex surface structures and huge variations of resolutions of aerial images, it is very challenging to retrieve aerial images with sketches and few studies have been devoted to this task. In this article, for the first time to our knowledge, we propose a framework to bridge the gap between sketches and aerial images. First, an aerial sketch-image database is collected, and the images and sketches it contains are augmented to various levels of details. We then train a multi-scale deep model by the new dataset. The fully-connected layers of the network in each scale are finally connected and used as cross-domain features, and the Euclidean distance is used to measure the cross-domain similarity between aerial images and sketches. Experiments on several commonly used aerial image datasets demonstrate the superiority of the proposed method compared with the traditional approaches.

Active learning for training sample selection in remote sensing image classification using spatial information

ABSTRACT Collection of training samples for remote sensing image classification is always time-consuming and expensive. In this context, active learning (AL) that aims at using limited training samples to achieve promising classification performances is developed. Recently, integration of spatial information into AL exhibits new potential for image classification. In this letter, an AL approach with two-stage spatial computation (AL-2SC) is proposed to improve the selection of training samples. The spatial features derived from remote sensing image and the probability outputs from the neighboring pixels are introduced in AL process. Moreover, we compare several AL approaches which take spatial information into account. In experiments, random sampling (RS) and four AL methods, including AL using breaking ties heuristic (BT), AL with spatial feature (AL-SF), AL with neighbouring responses (AL-NR), and AL-2SC, are considered. Three remote sensing datasets, including one hyperspectral and two multispectral images, are used to compare the performance of different methods. It is illustrated that, the utilization of spatial information is very important for the improvement of AL performance, and the proposed AL-2SC shows the most satisfactory result.

Classification of High-Resolution Remote-Sensing Image Using OpenStreetMap Information

Prior information about classes plays an important role in the high-resolution image classification. Produced by volunteers with GPS tracking practice and local knowledge, the crowdsourced OpenStreetMap (OSM) data have shown potential as a time-saving and cost-effective way to provide prior information for image classification. In this letter, we develop a high-resolution remote-sensing image classification method using OSM information. OSM objects of classes of interest except roads are extracted to construct the training set for classification. To decrease the misleading errors and redundancy in OSM, a series of approaches is employed successively to refine the training set. Furthermore, OSM road information is directly superimposed on the learned classification result owing to its good quality and completeness. The main contributions of this letter are: 1) the refinement of OSM-derived training samples and 2) the utilization of OSM road superimposition strategy. The high-resolution GF-2 image over the Guangzhou peri-urban area as well as the corresponding OSM data is employed in the experiments. The results illustrate the effectiveness of the proposed method.