Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

doi:10.11947/j.JGGS.2023.0106

Abstract

Abstract:

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method. The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline. Due to the differences in satellite sensors when producing images, subtle but inherent stripes can appear at the stitching positions between the sensors. These stitchingstripes cannot be eliminated by conventional relative radiometric calibration. The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation, classification and interpretation of remote sensing images. Therefore, a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper. First, the inconsistency in grayscales around stripes is eliminated with the statistical method. Second, the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality. Finally, the details of the images are highlighted by a new image enhancement method, which makes the whole image clearer. Comprehensive experiments are performed, and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

Key words: remote sensing images; stripe removal; statistics; image enhancement

Xiaofei QU,Weiwei ZHAO,En LONG,Meng SUN,Guangling LAI. Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(1): 76-87.

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Band	Range of wavelength
1	0.45~0.515
2	0.525~0.605
3	0.63~0.690
4	0.75~0.90
5	1.55~1.75
6	10.40~12.50
7	2.09~2.35
8	0.52~0.90

Number	Gaussian kernel	Wavelet	SGIDN	LRTD	SNIE
1	0.0372	0.0172	0.1313	0.1235	0.0023
2	0.0434	0.0272	0.1225	0.0532	0.0035
3	3.7109	23.9099	2.0105	3.0251	0.0769
4	8.4191	32.9777	1.0256	8.5941	0.0601
5	9.8381	9.8509	8.2562	10.2541	10.3380
6	6.3717	6.3710	6.6878	7.2356	6.8863
7	17.0192	17.0087	16.2548	15.2365	11.3915
8	23.7993	23.7631	24.0325	25.2360	18.1369
9	10.9678	10.9483	11.0213	10.2547	8.0407
10	25.6666	25.6317	24.2315	23.3256	18.1804
11	4.3382	4.3429	5.2365	4.5145	5.3600
12	21.4751	21.4403	20.0213	22.0352	19.9733
13	32.8196	32.7611	30.2123	33.0215	25.6624
14	15.9626	15.9518	16.2451	16.2354	15.0529
15	43.6562	43.5629	44.0365	42.0321	44.1033
16	7.8234	7.8165	8.3265	7.6589	10.1245
17	13.9280	13.9150	14.2315	14.2356	10.3044
18	11.0210	10.9964	12.3251	12.0354	14.5639
19	8.1372	8.1183	9.2013	10.2546	12.6522

Number	Gaussian kernel	Wavelet	SGIDN	LRTD	SNIE
1	4.7637	4.7616	5.2363	7.3654	3.5549
2	3.1473	3.1248	4.3210	9.3254	2.2915
3	4.4046	26.3350	5.3625	15.3247	2.0552
4	10.0775	37.8765	12.3652	11.2457	13.5879
5	12.6656	12.7311	15.3262	13.2154	16.2573
6	8.1723	8.2384	9.3265	9.1257	11.9527
7	20.0456	20.0796	21.0356	21.3254	18.3148
8	27.3236	27.3157	26.3265	28.2148	23.1276
9	14.7168	14.7552	15.3262	15.3654	14.8469
10	28.4846	28.4696	24.3265	29.1234	23.4003
11	7.8759	7.9296	8.3265	9.3258	12.8616
12	24.0951	24.0810	26.3256	25.8745	25.4896
13	38.2478	38.1988	36.0325	35.0214	32.5640
14	20.2182	20.2206	23.0325	20.3658	19.7066
15	47.8392	47.7343	48.3264	49.2541	48.8398
16	18.7895	18.7801	19.4578	19.6852	17.0141
17	19.7581	19.7465	19.8745	20.3652	12.0365
18	18.2736	18.2428	19.3654	21.3650	19.8136
19	14.5993	14.5811	15.3254	19.3254	17.3696

Number	GN without SNIE	GN with SNIE
1	0.0172	0.0003
2	0.0234	0.0181
3	0.0555	0.0554
4	0.1192	0.1191
5	0.1644	0.1519
6	0.0886	0.0795
7	3.8318	2.8595
8	2.4743	2.2915
9	3.5402	2.8990
10	6.3814	5.3970
11	4.1736	3.1562
12	4.6545	4.0483
13	6.8082	6.5225
14	2.6970	2.0589
15	3.4118	3.1374
Mean	2.5627	2.1863

Number	CoVa without SNIE	CoVa with SNIE
1	4.7647	3.5549
2	3.1513	2.8770
3	4.4414	3.6223
4	7.3563	6.0516
5	4.7837	3.384
6	5.5456	4.6846
7	7.9026	7.4591
8	3.4235	2.5169
9	4.1453	4.0979
10	3.6902	3.4323
Mean	4.9205	4.1681