At present, several algorithms have been developed to retrieve land surface temperature (LST) from remotely sensed imageries. Single-channel algorithms to estimate LST from one thermal band have ...advantage over other methods when the imageries with only one thermal band are used. However, accurate inputs including atmospheric parameters and land surface emissivity, which are always inaccessible to general users, are indispensable to the applications of the current single-channel algorithms. To solve this problem, a new single-channel method (an image based single-channel method called IBSC) based on the information inherent within the image itself, is proposed in this paper. The IBSC method is intended to retrieve LST directly from the sensor detected radiance, even with no accurate information about land surface emissivity and atmospheric parameters. Several HJ-1B imageries covering the Pearl River Delta and Wuhan City, China, were selected for LST estimation to assess the capability of the IBSC method. Results show that, by means of the IBSC method, sea/water surface temperature was retrieved with general bias about 1K compared with the MODIS products and the in situ measurements. Meanwhile, the traditional single-channel methods provided less accurate LST results when the plausibly estimated atmospheric parameters and surface emissivity were used as inputs. The inherent uncertainties and errors in the MODIS atmospheric products (e.g. MOD 05) are largely attributed to the incorrectness or insufficient improvement of the final LST results. Therefore, when there is no accurate information about atmospheric parameters and surface emissivity, the IBSC method is a good alternative for general users to make full use of the remotely sensed imageries with only one thermal band. Due mainly to the limited access to abundantly valid field data over land, in this paper, the 33 in situ measures over lakes as well as the sea/water surface temperature from MODIS products were only used to assess the retrieved HJ-1B results. The preliminary assessment positively suggests the applicability of the new method. However, to verify the accuracy and robustness of the IBSC method, more experiments and abundant reference data are required, especially for surface temperature over land. This innovative methodology may also be suitable for other sensors, such as Landsat 5/7/8.
A HYPERSPECTRAL IMAGE CLASSIFICATION METHOD USING ISOMAP AND RVM
The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences,
04/2018
Journal Article
The expansion of tree plantations in tropical forests for commercial rubber cultivation threatens biodiversity which may affect ecosystem services, and hinders ecosystem productivity, causing net ...carbon emission. Numerous studies refer to the challenge of reliably distinguishing rubber plantations from natural forest, using satellite data, due to their similar spectral signatures, even when phenology is incorporated into an analysis. This study presents a novel approach for monitoring the establishment and expansion of rubber plantations in Seima Protection Forest (SPF), Cambodia (1995–2015), by detecting and analyzing the ‘shapelet’ structure in a Landsat-NDVI time series. This paper introduces a new classification procedure consisting of two steps: (1) an exhaustive-searching algorithm to detect shapelets that represent a period for relatively low NDVI values within an image time series; and (2) a t-test used to determine if NDVI values of detected shapelets are significantly different than their non-shapelet trend, thereby indicating the presence of rubber plantations. Using this approach, historical rubber plantation events were mapped over the twenty-year timespan. The shapelet algorithm produced two types of information: (1) year of rubber plantation establishment; and (2) pre-conversion land-cover type (i.e., agriculture, or natural forest). The overall accuracy of the rubber plantation map for the year of 2015 was 89%. The multi-temporal map products reveal that more than half of the rubber planting activity (57%) took place in 2010 and 2011, following the granting of numerous rubber concessions two years prior. Seventy-three percent of the rubber plantations were converted from natural forest and twenty-three percent were established on non-forest land-cover. The shapelet approach developed here can be used reliably to improve our understanding of the expansion of rubber production beyond Seima Protection Forest of Cambodia, and likely elsewhere in the tropics.
An effective methodology for Bohai Sea ice detection based on gray level co-occurrence matrix (GLCM) texture analysis is proposed using MODIS 250m imagery. The method determines texture measures for ...sea ice extraction by analyzing the discrepancy of textural features between sea ice and sea water. Sea ice extent and outer edge are recognized accurately by texture segmentation owing to significant differences in texture statistical features between ice and water. The texture analysis method can properly eliminate perturbations on sea ice extraction due to suspended sediment. It effectively solves the problem of spectral confusion and sea ice misassignment in the conventional gray-threshold segmentation and ratio-threshold segmentation methods. The method eliminates the need for threshold range setting for sea ice segmentation. Taking the Bohai Sea as an example, the results of the proposed method are validated using co-temporal HJ1B-CCD 30m imagery by visual interpretation, and the accuracy of the method are evaluated using confusion matrix. The results show that the proposed method is superior and more reliable for sea ice detection compared to conventional methods, providing an ideal tool for precise sea ice extraction.
Hyperspectral anomaly detection methods based on representation model have attracted much attention in recent years. In the method, a background dictionary is used to represent each pixel linearly, ...and the residual is taken as the abnormal level of the pixel. Therefore, building a functional model with strong representation ability and a matching background dictionary is an important factor for success of the method. In the existing methods, the lack of feature utilization, the contamination of background dictionary and the dependence on prior knowledge lead to the instability of anomaly detection results, which are difficult to be applied in practice. To address the issues, this paper proposed a novel hyperspectral anomaly detection method which consists of two interconnected components: a new anomaly detection function model through the combination of LRR and CR, and the supporting background dictionary which does not need prior knowledge. The new anomaly detection model decomposed a two-dimensional normalized hyperspectral image into background and anomaly components, and reconstructed the background through a background dictionary and the corresponding coefficient matrix. The representation coefficient matrix was constrained by global low-rank and local collaborative attributes which helps background modeling. A distance weight matrix was also included to enhance the dense representation of background dictionary atoms which are similar to the testing pixel. The anomaly part was constrained by column sparsity simultaneously due to the global sparsity of anomalous targets in hyperspectral images. To further clarify the physical meaning of the model, another version that imposes non-negative and sum-to-one constraints on coefficient matrix was also proposed. The supporting background dictionary was designed for practical and reliable purposes and was implemented by dual mean shift clustering which automatically estimates parameter without prior-knowledge. The experimental results show that the proposed method can effectively improve the result of anomaly detection, and has the best ROC, AUC, and separation degree between background and anomalies for four real datasets. The average AUC of four datasets of the proposed algorithm is 2.6% higher than the method based on CR and 3.2% higher than the method based on LRR. Moreover, the proposed algorithm has stability and reliability under complex background, as well as the application on a larger hyperspectral scene shows that it offers great potential in practical use.
VISUAL UAV TRAJECTORY PLAN SYSTEM BASED ON NETWORK MAP
The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences,
07/2012
Journal Article
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